Discontinuous Combinatory Constituency Parsing

Zhousi Chen and Mamoru Komachi
Faculty of Systems Design
Tokyo Metropolitan University
6-6 Asahigaoka, Hino, Tokio 191-0065, Japón
{chen-zhousi@ed., komachi@}tmu.ac.jp

Abstracto

We extend a pair of continuous combinator-
based constituency parsers (one binary and
one multi-branching) into a discontinuous
pair. Our parsers iteratively compose con-
stituent vectors from word embeddings with-
out any grammar constraints. Their empirical
complexities are subquadratic. Our extension
includes 1) a swap action for the orientation-
based binary model and 2) biaffine attention
for the chunker-based multi-branching model.
In tests conducted with the Discontinuous
Penn Treebank and TIGER Treebank, nosotros
achieved state-of-the-art discontinuous accu-
racy with a significant speed advantage.

Introducción

Discontinuity is common in natural languages,
as illustrated in Figure 1. Children from a discon-
tinuous constituent are not necessarily consecu-
tive because each can group with its syntactic
cousins in the sentence rather than its two adja-
cent neighbors. Although this relaxation makes
discontinuous parsing more challenging than con-
tinuous parsing, it becomes more valuable for
studies and applications in non-configurational
idiomas (Johnson, 1985), where word order does
not determine grammatical function. With gradu-
ally saturated continuous parsing accuracy (zhou
and Zhao, 2019; Kitaev and Klein, 2018, 2020;
Xin et al., 2021), discontinuous parsing has started
gaining more attention (Fern´andez-Gonz´alez and
G´omez-Rodr´ıguez, 2020a, 2021; Corro, 2020).

Typically, constituency parsers are divided into
two genres (not including methods employing de-
pendency parsing, p.ej., Fern´andez-Gonz´alez and
G´omez-Rodr´ıguez [2020b]): 1) Global parsers use
a fixed chart to search through all parsing possi-
bilities for a global optimum. 2) Local parsers
rely on fewer local decisions, which leads to
menores complejidades. The global parser complexi-

ties start at least from binary O(n3) (Kitaev and
Klein, 2018) or m-ary O(n4) (Xin et al., 2021),
resulting in low speeds for long parses. Global
parsers introduce numerous hypotheses, whereas a
local shift-reduce process or incremental parse ex-
hibits more linguistic interests with fewer outputs
(Yoshida et al., 2021; Kitaev et al., 2022). nuevo-
ral global parsers dominate both continuous and
discontinuous parsing (Corro, 2020; Ruprecht and
M¨orbitz, 2021) in terms of F1 score, but they do
not exhibit a strong accuracy advantage over other
analizadores. Although local parsers may in some cases
produce ill-formed trees, global parsers do not
guarantee the selection of gold-standard answers
from their charts.

We propose extending a pair of local parsers
to achieve high speed, exactitud, and convenient
investigation for both binary and multi-branching
parses. Chen et al. (2021) proposed a pair of
continuous parsers employing bottom-up vector
composicionalidad. We dub these as neural combi-
natory constituency parsers (NCCP) with binary
CB and multi-branching CM. To the best of our
conocimiento, they possess the top parsing speeds
for continuous constituency. CB reflects a linguis-
tic branching tendency, whereas CM represents
unsupervised grammatical headedness through
composition weight. CM is among the few parsers
that do not require preprocessing binarization
(Xin et al., 2021) but possess a high parsing speed.
We dub our extension as DCCP with binary DB
and multi-branching DM. The mechanisms with
neural discontinuous combinators are shown in
Cifra 2.

Específicamente, our combinators take a sentence-
representing vector sequence as input and pre-
dict layers of concurrent tree-constructing actions.
Two mechanisms are employed. DB triggers an
action if the orientations of two neighboring vec-
tors agree. A swap action exchanges the vectors;

267

Transacciones de la Asociación de Lingüística Computacional, volumen. 11, páginas. 267–283, 2023. https://doi.org/10.1162/tacl a 00546
Editor de acciones: Carlos G´omez-Rodr´ıguez. Lote de envío: 8/2022; Lote de revisión: 12/8/2022; Publicado 3/2023.
C(cid:2) 2023 Asociación de Lingüística Computacional. Distribuido bajo CC-BY 4.0 licencia.

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complexity h. Como consecuencia, a binary continuous
h=0(n − h) · h ∼ O(n3)
chart parser has a fixed Σn
complexity for the CKY decoding algorithm.

In m-ary and/or discontinuous cases (es decir., para
multi-branching arity m and/or fan-out k of each
constituent), the chart is superior to that of bi-
nary continuous parsing in terms of complexity.
Both horizontal and vertical axes expand to di-
versify the combination of discrete bits of each
lexical node. Because of the expansion, m-ary
continuous global parsing (Xin et al., 2021) tiene
oh(n4) complejidad, whereas binary discontinuous
parsing has exponential complexity at O(n3k)
(Corro, 2020; Stanojevic and Steedman, 2020),
where k ∈ {1, 2} are special cases for binary CFG
(likely in Chomsky Normal Form [CNF]) and bi-
nary Linear Context-Free Rewriting Systems with
maximum fan-out 2 (Stanojevic and Steedman,
2020, LCFRS-2). M -ary discontinuous parsing,
which certainly has a higher complexity, is not yet
available for global parsing.

For efficiency, expensive rules are commonly
restricted by LCFRS-2 parsers (Corro, 2020;
Stanojevic and Steedman, 2020; Ruprecht and
M¨orbitz, 2021). A tricky O(n3) variant of Corro
(2020) covering major rules has produced the best
resultados. Sin embargo, the variant excludes 2% sophis-
ticated discontinuous rules on TIGER Treebank.
Limited by the simplified grammar, their discon-
tinuous scores are low, especially for recalls. A
global optimum does not guarantee a gold parse,
leaving room for local parsing.

Local Parsing. Local parsers do not observe the
chart framework and only consider one greedy
or a few hypotheses. Transition-based parsers
with a swap or gap action have sequential actions
and low complexities (Maier, 2015; Coavoux and
Crabb´e, 2017). Multiple swaps or gaps combine
to construct a large discontinuous constituent. En
contrast, stack-free parsing can directly pick up
a distant component with one attachment search
(Coavoux and Cohen, 2019). Easy-first (Nivre
et al., 2009; Versley, 2014) and chunker-based
(Ratnaparkhi, 1997; Collobert, 2011) run rapidly.
Fern´andez-Gonz´alez and G´omez-Rodr´ıguez
(2020a) redirected discontinuity to dependency
parsing via pointer networks and obtained signifi-
cant accuracy improvement among greedy parsers.
Sin embargo, it is difficult to determine whether such
improvement originates from the model or the
extra head information. A diferencia de, Fern´andez-

Cifra 1: Evang and Kallmeyer (2011, DPTB) re-
cover discontinuity from continuous Penn Treebank
(Marcus et al., 1993, PTB) with trace nodes (azul).

a joint action composes a new vector with them.
CB only possesses a joint action. Mientras tanto, DM
takes discontinuous vectors to form biaffine at-
tention matrices and decides their groups collec-
activamente; the remaining continuous vectors resort
to chunking decisions, as with CM. NCCP and
DCCP are unlexicalized supervised greedy parsers.
The contributions of our study are as follows:

• We propose a pair of discontinuous parsers1
(es decir., binary and multi-branching) by ex-
tending continuous parsers of Chen et al.
(2021).

• We demonstrate the effectiveness of our work
on the Discontinuous Penn Treebank (Evang
and Kallmeyer, 2011, DPTB) and the TIGER
Treebank (Brants et al., 2004). Our parsers
achieve new state-of-the-art discontinuous
F1 scores and parsing speeds with a small
set of training and inferring tricks, incluido
discontinuity as data augmentation, unsuper-
vised headedness, automatic hyperparameter
tuning, and pre-trained language models.

2 Trabajo relacionado

Global Parsing. The chart for binary continuous
analizando (Kitaev and Klein, 2018) is triangular,
as shown in black in Figure 3. The horizontal
dimension enumerates the position of each node
(es decir., each bit as a word). The vertical C indicates
the number of continuous cases included in each
nodo. Nodes at height h share combinatorics of

1 Our code with all model configuration files is available

at https://github.com/tmu-nlp/UniTP.

268

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Cifra 2: Mechanism examples. Left: DB produces swap to facilitate traveling of discontinuous nodes and joint
to combine adjacent nodes. Right: DM leverages biaffine attention to identify and combine discontinuous groups.

Cifra 3: Fan-out and input sizes increase Continuous
and Discontinuous combinatory global parser cases.

Gonz´alez and G´omez-Rodr´ıguez (2020b) reor-
dered input words in at most O(n2) complejidad
and redirected to various continuous parsers.

3 Discontinuous Combinatory Parsing

We call a level of partial derivations or subtrees
a ply (Jurafsky and Martin, 2009), as depicted in
Cifra 4. Similar to the state of a transition-based
parser, each of which leads to an action, ply is the
state for DCCP, each of which leads to a sequence
of concurrent actions for itself.

Starting with a sequence of words (x1, · · · , xn)
as an initial ply, we assemble an unlabeled dis-
continuous parse tree in a bottom–up manner by
applying concurrent actions to the roots of sub-
trees in the ply and iterating until sequence length
norte = 1.

3.1 Binary Ply: Joint and Swap

For a binary tree, two actions are sufficient:

acción(xi ⊕ xi+1) ∈ {articulación, swap}
articulación : compose(xi, xi+1) → xi
swap : (xi, xi+1) → (xi+1, xi) .

(1)

Cifra 4: DCCP plies with concurrent actions.

One joint reduces the sequence length by one;
the swap does not affect the sequence length but
affects its order. The binary function compose is
a binary neural combinator. The concatenation
‘‘⊕’’ only works for adjacent nodes.

Sin embargo, concurrent adjacent actions would
conflict in a ply (p.ej., two swaps for (x1, x2, x3)
leaves an undecidable x2). En otras palabras, ellos
need a resolution. We adopt a solution of

orientación(xi) ∈ {0, 1}
orientación(xi) − orientation(xi+1) = 1 ,

(2)

where each orientation indicates either left (0) o
bien (1) and the adjacent node pair of (xi, xi+1)
contains the agreeing orientations. De este modo, only un-
der this circumstance, DB activates joint or swap
by action(xi ⊕ xi+1) without conflict.

Summary. All nodes in a DB ply are derived
by Formula 1 under the condition of Formula 2
to form a new ply. As exemplified in Figure 4
for DB, (x1, x2) y (x4, x5) meet the condition
of Formula 2 and have respective joint and swap
comportamiento. Mientras tanto, x3 takes neither action and
remains in the ply, because the orientations of x2

269

and x4 do not agree with x3, regardless of x3’s
orientación.

3.2 Multi-branching Ply: Affinity and Chunk

We characterize whether xi and xj from a ply are
two siblings of the same parent constituent as

affinity(xi, xj) ∈ {0, 1} ,

(3)

dónde 0 denotes false and 1 denotes true. De este modo,
DM decides a discontinuity action of xi and
then forwards it
to a group action for either
a discontinuous or continuous constituent as in
Formula 4:

acción(xi) ∈ {discontinuous, continuous}
discontinuous :

G = {xj | affinity(xi, xj) = 1}
medoid ∈ {j | xj ∈ G}
compose(GRAMO) → xmedoid

(4)

continuous :
G = {xj | lb < i ≤ rb, lb < j ≤ rb, and affinity(xj, xj+1) = 1(j /∈ {lb, rb})} compose(G) → xlb+1 , whereas ‘‘1(·)’’ is the indicator function. We select one medoid for each discontinuous con- stituent to determine its position in the modified ply, whereas the choice of medoid for continu- ous constituents makes no difference. Continuous · · ·, xrb) nodes split into segments of (xlb+1, lb + 1) and (rb, rb + 1) as bound- with (lb, aries. Function compose is a flexible m-ary neural combinator with m ∈ N. Dozat and Manning (2017) characterized each dependency tree as a sparse asymmetric matrix via biaffine attention, with each sole positive signal in a row (or column) indicating a lexical depen- dency (from a word to its head or vice versa). Nevertheless, lexical dependency is not available for constituency parsing, and biaffine attention becomes expensive at O(n2) complexity. In contrast, we designate discontinuous affinity as a small dense symmetric biaffine attention matrix and control its computational size of O(n2). Otherwise, continuous affinity for adjacent nodes takes a special form of chunk(xi ⊕ xi+1) = affinity(xi, xi+1) with a simpler O(n) complexity. Summary. Via fast chunking or a small bi- affine attention matrix, DM balances to increase its efficiency. As exemplified in Figure 4 for DM, discontinuous (x1, x2, x5) are grouped as one because of their mutual affinity, which is equivalent to a 3 × 3 biaffine attention matrix of ones. Node x2 is selected as the medoid for the constituent’s location in the new ply. Mean- while, continuous (x3, x4) forms a constituent for chunk(xi ⊕ xi+1) = affinity(xi, xi+1) = 1(i /∈ {2, 4}) and i ∈ [2, 4]. 3.3 Oracle We state the conversion from tree into layers of action signals for fully supervised training. For convenience, we merge DM’s chunk seman- tics into joint to unify DB’s and DM’s interstice signals. The common signals are (cid:2) x1:n, t1:n, l1:H 1:nh , j1:H−1 1:nh−1 (cid:3) , where x represents a sentence with n words, n POS tags, H layers of labels, and H − 1 layers of joints, respectively. ‘‘:’’ indicates sequence range (e.g., ply height h ∈ [1, H]). DB features in (cid:2) (cid:3) o1:H−1 1:nh , containing H − 1 layers of orientations. For DM, our extension includes discontinuity and affinity biaffine attention matrices with medoids, (cid:4) d1:H−1 1:nh , a1:H−1 [1:dh, 1:dh] (cid:5) , (cid:6) nh i=1 dh i ≤ nh indicates a number where dh = of discontinuous nodes that is no larger than the number of total nodes in layer h. Empty Node and Unary Branch. Similar to a range of previous works (Chen et al., 2021; Shen et al., 2018; Kitaev and Klein, 2020; Corro, 2020), we adopt an empty label ‘‘∅’’ for our substructure (e.g., binarization). Additionally, we collapse each unary branch into a single node and join their constituent labels according to their hierarchical order (e.g., S+VP as derivation S→VP) with easy restoration during inference. Unary collapse is productive for label type, as shown in Table 1. Binarization ρDB. C children of a constituent join one by one via their orientation and joint signals. For c ∈ [1, C), [1, c]-th children are set to orientation right (1) and (c, C]-th are set to 270 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / t a c l / l a r t i c e - p d f / d o i / . 1 0 1 1 6 2 / t l a c _ a _ 0 0 5 4 6 2 0 7 5 7 3 9 / / t l a c _ a _ 0 0 5 4 6 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / t a c l / l a r t i c e - p d f / d o i / . 1 0 1 1 6 2 / t l a c _ a _ 0 0 5 4 6 2 0 7 5 7 3 9 / / t l a c _ a _ 0 0 5 4 6 p d . Figure 5: Examples illustrating the principle of stratification. The original m-ary tree (c) is binarized and stratified into (a) and (b) with numeric factors ρDB, whereas (c) is stratified into (d) with a categorical medoid factor random. In (d), w2 and w5 are randomly selected as medoids for discontinuous parents l2 2 with more or less twisted descendant lines. We color constituent components and show disabled joints with light blue ‘‘ ’’ and ‘‘(cid:8).’’ 1 and l2 Corpus DPTB TIGER Label Type Total Collapsed Label Token Frequency% 126 80 99 55 3.82% 0.72% Table 1: Label type and token in our oracle format. orientation left (0). Neighboring children have positive joint signals if they are siblings. Oth- erwise, negative joints swap them toward their siblings. We normalize a factor ρDB = c−1 ∈ C−2 [0, 1] for treebank binarization. In continuous parsing, ρDB ∈ {0, 1} implies CNF. As illustrated in Figure 5 (a) & (b), we obtain layers of action signals from the binarization of (c). As an extension to CNF, we use the beta distri- bution ρDB ∼ Beta(αleft, αright) ∈ (0, 1) to create augmented samples with αleft, αright ∈ (0, +∞). Medoid ρDM. We use a set of categorical medoid factors ρDM ∈ {random, leftmost, rightmost} to stratify a multi-branching tree: 1) random picks a random child with uniform probability, whereas 2) leftmost and 3) rightmost take the two ends of a discontinuous group. 2 and l2 In Figure 5 (d), w1 and w4 are randomly selected as medoids. Meanwhile, l1 2 would exchange their places if w3 and w4 were selected. Medoid is different from headedness (Zwicky, 1985). It is an intermediate variable for locating a discon- tinuous constituent. 3.4 Model Implementation NCCP and DCCP have the same bottom–up iteration on a ply and share two types of neu- Algorithm 1: Combinatory Parsing 1 Function PARSE(x1:n): 2 ply ← [] and nh ← n with height h ← 1; xh 1:nh for i ← 1 to n do ← BiLSTMcxt(embeddings of x1:n); i ); ← FFNNlabel(xh ˆti ← FFNNtag(xh ˆlh i append a tree with (xi, ˆti, ˆlh i ); i ) to ply; while nh > 1 hacer

);

← BiLSTMply(xh
1:nh
← FOLD(capa, xh

zh
1:nh
xh+1
1:nh+1
h ← h + 1;
for i ← 1 to nh do

, zh

1:nh

);

1:nh

← FFNNlabel(xh

ˆlh
i
label i-th tree of ply with ˆlh
i ;

i );

return the sole tree in ply;

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ral components: bidirectional Long Short-Term
Memoria (BiLSTM) and feedforward neural net-
trabajar (FFNN).

In Algorithm 1, BiLSTMcxt contextualizes the
sequence of words x1:n as embeddings x1
1:n1 and
BiLSTMply contextualizes the ply sequence xh
1:nh
for either DB FOLD in Algorithm 2 or DM FOLD
in Algorithm 3, either of which modifies ply and
constructs new layers of embeddings xh+1
. El
necessity of BiLSTMcxt and BiLSTMply contex-
tualization was empirically examined with NCCP.
Mientras tanto, FFNNtag and FFNNlabel predict the
lexical tags and constituent labels based on contex-
tualized individual embeddings without grammar
constraint.

1:nh+1

When actions do not modify ply at inference
phase, PARSE terminates with a VROOT label
for the current ply. We define CONDENSE as a

271

Algoritmo 2: Binary Ply
1 Function FOLD(capa, x1:norte, z1:norte):
2

for i ← 1 to n do

← FFNNori(zi);
← FFNNjoint(zi ⊕ zi+1);

ˆoh
i
ˆjh
i
wrt. Subsection 3.1, apply actions to ply
and xi ← COMPOSE(xi, xi+1);

return CONDENSE(x1:norte);

7 Function COMPOSE(xL, xR):
8

λ ← σ FFNNbinary(xL ⊕ xR);
return λ (cid:11) xL + (1 − λ) (cid:11) xR;

process reenumerating ply nodes regardless their
inconsecutive and unordered indices following
the actions (p.ej., CONDENSE(x1, x3, x5, x4) →
(x1, x2, x3, x4))

CONDENSE : (n ply nodes) → (x1, . . . , xn)

Binary Combinator. The FOLD of DB is shown
in Algorithm 2. The COMPOSE function uses
sigmoid activation ‘‘σ’’ to create a pair of com-
plementary gates λ and (1 − λ) for xL and xR. λ
is a vector of the same size as the embeddings.

Multi-branching Combinator. The DM FOLD
has vectors z1:n and Δ1:n in exchangeable shapes
in Algorithm 3. We choose Δ1:n because it per-
forms empirically optimal. De lo contrario, Algorithms
2 y 3 should look more identical. Mientras tanto,
(cid:6)GRAMO
λi in COMPOSE with
i λi= 1 from Softmax
is the adaptive gating vector for xi. We consider
the average of λi (es decir., ¯λi) as the unsupervised
headedness for inference and visualization. De este modo,
DM is able to infer with a special factor:

ρDM = uhead ,

which takes medoid ← arg maxi∈G ¯λi as the
group medoid.

[1:D,1:D] for value range (0, 1) (re =

To identify discontinuous groups in the affin-
ity biaffine attention matrix, DM takes M =
ˆdh
σˆah
i )
and booleanizes it into B ← M > θ. Él 1) intentos
default threshold θ = 0.5 as the natural selection
for sigmoid activation and checks whether all the
following statements are true:

(cid:6)

• B is symmetric (es decir., B = B(cid:2)),
• any rows v, w ∈ B are v (cid:12)= 0,
• either v = w or v(cid:2) · w = 0.

Algoritmo 3: Multi-branching Ply
1 Function FOLD(capa, x1:norte, z1:norte):
2

((cid:7)z1:n ⊕ (cid:7)z1:norte) ← z1:norte (forward & backward);
for i ← 1 to n do

← FFNNdisc(zi);

ˆdh
i
Δi ← ((cid:7)zi − (cid:7)zi−1) ⊕ ((cid:7)zi − (cid:7)zi+1);
ˆjh
← FFNNjoint(Δi ⊕ Δi+1);
i
v,w do
· W aﬀ · Δw + baﬀ ;

foreach i, j-th discontinuous ˆdh

← x(cid:2)
v

ˆah
[i,j]

find discontinuous groups by checking ˆah;
foreach group indices G and its medoid do

w.r.t Subsection 3.2, apply actions to ply
and xmedoid ← COMPOSE(ΔG, xG);

return CONDENSE(x1:norte);

13 Function COMPOSE(ΔG, xG):
14

λG ← Softmax (FFNNmulti(ΔG));
return

(cid:6)GRAMO

i λi (cid:11) xi;

It succeeds in most cases. De lo contrario, él 2) intentos
a value from M as θ, checks and loops again.
We order the thresholds by their distances to the
default 0.5. Me caigo 2) fail, él 3) simply falls back into
grouping all nodes as one and counts one FAIL.

Basic Losses. We choose HINGE-LOSS for
binary prediction and CROSS-ENTROPY for
multi-class prediction, following NCCP. Respect-
ing the context in Algorithms 1–3, our basic loss
items are

Ltag, Llabel, Lori, Ljnt, Ldisc, LD
aff

(cid:6)

by accumulating their items across all layers.
Por ejemplo, LD
[i,j],
aff
ˆah
[i,j]) with D for discontinuous affinity. Tenemos
additional loss items in the next subsection.

i,j,h HINGE-LOSS(ah

←

Complexity. Extreme cases provide the upper
bounds for our theoretical complexity. DB takes
2 fully swapping plies and n
norte
2 fully joining plies.
Each ply costs O(norte) recurrency; the bound is
oh(n2). Every DM ply involves a matrix for all
nodes and decreases n only by one. Assume that
we limit check 2) to some fixed sizes. Each ply
costs O(n2); the bound is O(n3).

Sin embargo, DCCP has an empirical O(n2) com-
plexity with strong linearity, as shown in Figure 6.
DB has higher linear coefficients because of its
slow binary combination. Mientras tanto, DM shows
stronger quadratic tendency because of biaffine

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Cifra 6: Quadratic linear regression (LR) on sentence
length vs. parsing node count on stratified DCCP tree-
banks. DM counts in biaffine attention matrix nodes.
Colors show binarization and medoid strategies. Cubic
LR gives all negative cubic terms highly close to zero.

atención. Todavía, their coefficient magnitudes are on
par with one another.

3.5 Training Tricks

Data Augmentation. Cifra 7 first summarizes
(mi) basic data augmentation with binarization for
DB and medoid for DM (incluido (a), (b), y (d)
En figura 5). The beta distribution can resemble a
uniform random distribution or other biased dis-
tributions to detect linguistic branching tendency
with specific (αleft, αright).

Entonces, we further leverage the intermediate non-
terminal node ‘‘∅’’ to create more ∅-subtrees.
The augmentation is an inspiration of CM’s de-
terministic SUB node, which balances subtree
heights and boosts both accuracy and efficiency.
(No- SUB trees remain at their original heights.)
Sin embargo, (gramo) ∅-subtree is random and creates
imbalance. It creates only one stretching branch
by iteratively grouping nodes with possibility ρ∅,
which has three significant impacts:

• Random stretching branches add mild vari-
ations to the context as states for robust ply
actions in FOLD.

• Random discontinuity creates DB orienta-
tion layers that cannot be created by ρDB
binarization.

• They reduce large (possibly continuous)
constituents into smaller (possibly discon-
tinuous) pieces without adding a large pay-
load to the biaffine attention, which narrows
the gap between DB and DM (DM is
more vulnerable to dramatic many-to-one
COMPOSE).

Taking NP ‘‘a good day’’ for instance, any of
‘‘a day,’’ ‘‘a good,’’ and ‘‘good day’’ can be an

intermediate option for creating the NP. Sobre el
one hand, these options create varied contexts for
the remaining parts of a ply. Por otro lado,
assume that ‘‘a day’’ (which is not a ρDB product)
is selected. DM learns to discern it with the other
possible ‘‘a day’’ in biaffine attention based on
their context.

ori and Lshfl

jnt. It shuffles xh

Model Robustness. To further randomize DB
training, we introduce (F) ply shuffle and its resul-
tant losses Lshfl
con
respect to each constituent, takes the new sequence
to BiLSTMply and FOLD, and reuses the ply of
orientation and joint for those additional losses.
Por ejemplo, a VP to the left of an NP gets shuf-
fled to the right with the same ply ‘‘right (cid:3) left’’
producing the additional loss items.

1:nh

Continuous affinity and discontinuous affinity
in DM undergo different identification processes.
To minimize the difference, we introduce (h) LC
aff
and LX
aff for continuous and interply affinity, en
addition to cardinal LD
aff. These reduce the risk
of biaffine attention forwarding incorrect nodes,
which would evoke exposure bias. We use posi-
i ) and βx · σ(xh(cid:2)
tive rates βc and βx for βc · σ( ˆdh
·
W aff · Δ¯h
w + baff) to limit the sample size, dónde
layers ¯h (cid:12)= h contain discontinuous nodes. Fal-
lible signals are more likely to form losses via
HINGE-LOSS.

En resumen, the additional loss items are

ori, Lshfl
Lshfl

jnt, LC

aff, LX
aff .

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4 Experimento

DCCP takes frozen pre-trained FastText as static
incrustación de palabras (PWE) or fine-tuned 12-layer
pre-trained XLNet and BERT as contextualized
embedding of pre-trained language models (PLM)
as lexical input 2 and parses on English DPTB and
German TIGER treebanks. See Table 2.

Two-stage Training for a PWE Model. El
first stage (S1) requires approximately 300 epochs

2To compare to other parsers with a lexical component,
NCCP used pre-trained FastText or FastText trained on PTB.
The former slightly increased F1 score by 0.2. We choose
BERT (https://www.deepset.ai/german-bert)
for German and adopt FFNNcxt instead of BiLSTMcxt for
model connection following NCCP.

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(mi) Beta distribution is equivalent to uniform random when αleft = αright = 1. De lo contrario, es
Cifra 7:
for detecting branching tendency with DB. (F) Constituent children get shuffled and create additional losses.
(A1, A2, A3), B1, y (C1, C2) belong to three different constituents. (gramo) ∅-subtree creates discontinuity from
continuity with a random stretching branch. (h) In-ply continuous nodes and interply nodes are chosen for DM
biaffine attention.

DCCP model dimension

BiLSTMctx / BiLSTMply layers
FFNN{tag, label, ori, jnt, disc} capas

300
6 / 2
2
Optimizer
Adán
(1-epoch γ warm-up, decaimiento lineal, and early stop.)

The second stage (S2) involves 100 short trials
with a Bayesian optimization (BO) tool (Akiba
et al., 2019, optuna); each trial requires less than
30 epochs and brings hyperparameter adjustment:

Drop out rate (recurrent)
Batch size (non-training)

Parameter sizes (w/o PLM)
BiLSTMcxt

+CB

+CM +DB

0.4 (0.2)
80 (160)

+DM

3.25METRO

0.36M 0.55M 1.32M 1.45M

Mesa 2: Fixed model hyperparameters and model
parameter sizes of NCCP and DCCP. CB & CM
have a single-layer BiLSTMply of fewer model
parámetros.

with general hyperparameters. Loss functions are
sum of all loss items:

DB = Ltag + Llabel + Ljnt + Lori + Lshfl
LS1
DM = Ltag + Llabel + Ljnt + Ldisc +

ori + Lshfl
jnt
(cid:7)
li
aff

LS1

i∈D,X,C

Adam optimizer’s learning rate is γ = 10−3.
DB uses uniform binarization αleft = αright = 1.
DM uses ∅-subtree ratio ρ∅ = 0.25, robustez
βc = 0.1, and βx = 1 for both efficiency and
exactitud.

274

DB = αtag · Ltag + · · · + αshfl
LS2
jnt
(cid:7)
DM = βtag · Ltag + · · · +
LS2

· Lshfl
jnt
(βi · Li

aff).

i∈D,X,C

Trials follow practical constraints: learning rate
γ ∈ (10−6, 10−3), beta’s αleft, αright ∈ (10−3, 103)
instead of (0, +∞), y [0, 1] for the others.

PLM models also use general hyperparameters
with learning rate 10−6 at S1. PLMs are frozen
during the first 50 epochs to avoid noise pollution
and then are fine-tuned with learning rate 3×10−6.
They inherit explored hyperparameters from PWE
models at S2, except for learning rate 3 × 10−6.

4.1 Overall Results

Mesa 3 shows F1 scores of recent neural dis-
continuous parsers under comparable conditions
on test sets. We follow their reported number of
significant digits and reduce the effects of random
initialization with an average of five runs. El
details are shown in Table 4.

DCCP models achieved state-of-the-art per-
formance in terms of discontinuous F1 scores
and parsing speeds. Although speed tests are

Tipo
Trans-Gap
Stack-Free
Seq-Labeling

Modelo
without pre-trained language model
Coavoux et al. (2019)
Coavoux and Cohen (2019)
Pointer-based VG20 w/ Ling et al. (2015)
Pointer-based FG22 w/ Ling et al. (2015) Multitask†
Stanojevic and Steedman (2020)
Corro (2020)
Ruprecht and M¨orbitz (2021) w/ flair
DB w/ FastText (en & de)
DM w/ FastText (en & de)
with pre-trained language model
Pointer-based VG20 w/ BERTBASE
Pointer-based FG22 w/ BERTBASE
Corro (2020) w/ BERT
Ruprecht and M¨orbitz (2021) w/ BERT
FG21 w/ XLNet (en) or BERTBASE (de)
FG21 w/ XLNet (en) or BERTBASE (de)
DB w/ XLNet (en) or BERTBASE (de)
DM w/ XLNet (en) or BERTBASE (de)

Seq-Labeling
Multitask†
Chart
Chart
Reorder-Chart
Reorder-Trans
Combinator
Combinator

Chart
Chart
Chart
Combinator
Combinator

Complexity
oh(norte)
oh(n2)
oh(n2)
oh(n2)
oh(n6)
oh(n3)
–
oh(n2)
oh(n3)

DPTB test set
D.F1
71.3
67.3
45.8
–
67.1
64.9
76.1
75.6
78.1

Speed
80
38
611
–
–
355
86
940
970

F1
91.0
90.9
88.8
–
90.5
92.9
91.8
92.0
92.1

TIGER test set
D.F1
55.9
55.9
39.5
62.6
53.5
51.2
61.0
60.1
62.0

Speed
126
64
568
–
–
474
80
1160
1300

F1
82.7
82.5
77.5
86.6
83.4
85.2
85.1
84.9
85.1

oh(n2)
oh(n2)
oh(n3)
–
oh(n3)
oh(n2)
oh(n2)
oh(n3)

91.9
–
94.8
93.3
95.1
95.5
94.8
95.0

50.8
–
68.9
80.5
74.1
73.4
76.6
83.0

80
–
–
57
179
133
275
375

84.6
89.8
90.0
88.3
88.5
88.5
89.5
89.6

51.1
71.0
62.1
69.0
63.0
62.7
69.7
70.9

80
–
–
60
238
157
424
535

Mesa 3: Overall performance of recent discontinuous parsers. Speeds in sentences per second
were obtained in tests conducted on incomparable hardware and software platforms. Ours and
Vilares and G´omez-Rodr´ıguez (2020, VG20) were conducted on a GeForce GTX 1080 Ti with a
PyTorch implementation, and that of Fern´andez-Gonz´alez and G´omez-Rodr´ıguez (2021, FG21) era
conducted on a GeForce RTX 3090. Fern´andez-Gonz´alez and G´omez-Rodr´ıguez (2022, FG22) involved
lexical dependency information†.

PWE

F1
DPTB (prueba)
91.97±0.05
DB
DM 92.06±0.10
94.84±0.24
DB
DM 95.04±0.06

PLM

PWE

F1
TIGER (prueba)
84.88±0.08
DB
DM 85.11±0.13
89.48±0.16
DB
DM 89.61±0.09

PLM

D.F1
75.62±0.82
78.14±0.69
76.62±2.07
83.04±0.79
D.F1
60.08±0.37
62.02±0.71
69.68±0.55
70.93±0.63

Mesa 4: Means and standard deviations of five
runs on test sets with four significant digits. DM
outperforms DB. Development sets reflect similar
variabilidad.

conducted on different platforms, our parsers lead
by a significant margin. In terms of overall F1
puntaje, our parsers outperform some chart parsers
(Stanojevic and Steedman, 2020; Ruprecht and
M¨orbitz, 2021) and slightly underperform the
overall best outline, as characterized in boldface.

4.2 Ablation Study

We ablate the PWE models in two-stage training,
as shown in Table 5. We only show one repre-

275

Modelo
(Stage)

DPTB (desarrollador)
D.F1
F1

TIGER (desarrollador)
D.F1

(S1)
(S2)

(0, 0, 0)
(0, 1, 1)
(1, 0, 1)
(1, 1, 0)
‡ (1, 1, 1)
(cid:10)→ optuna

(0, 0, 0)
(0, 1, 1)
(1, 0, 1)
(1, 1, 0)
‡ (1,1,1)
(cid:10)→ optuna

90.93
91.61
91.62
91.48
91.72
92.25

91.62
91.44
91.74
91.84
92.16
92.37

63.28
69.84
74.25
70.97
66.82
76.60

79.37
78.70
79.02
77.37
80.29
82.76

87.73
88.70
87.93
89.05
89.28
89.59

88.30
88.61
89.64
89.78
89.77
89.84

56.49
61.15
59.85
63.32
63.49
66.03

62.41
65.10
67.40
67.78
68.20
68.45

Mesa 5: Ablation in two-stage training on de-
velopment F1 scores. Triplets in {0, 1} indicate
turning on and off (ρ∅, ρDB ∼ Beta(1, 1), shuffle)
for DB and (ρ∅, βc, βx) for DM. Variants of
‘‘‡’’ are S1—the start of S2.

sentative run with ablation because of the similar
low variability on development sets. DB has two
data augmentation items ρ∅ and ρDB as well as one
model item ply shuffle. On ρ∅ refers to ρ∅ = 0.25
and off ρ∅ = 0. Off Beta(1, 1) refers to a static

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Modelo

Dev set

ρ∅ = 0

0.1

DPTB
TIGER

91.61
88.70

91.44
88.61

91.79
89.04

91.80
89.45

‡0.25

91.72
89.28

92.16
89.77

0.5

91.95
89.25

89.86
88.61

Mesa 6: DM is sensitive to ρ∅ with dev F1 scores.
All variants are based on (ρ∅, 1, 1) en mesa 5
with specific ρ∅ as the variable for DB and DM.

Test set DM Medoid ρDM

DPTB

TIGER

uhead
leftmost
rightmost
aleatorio (mín.)
aleatorio (máximo)

95.05
95.00
95.03
95.01
95.04

89.62
89.56
89.56
89.55
89.61

D.F1

83.58
81.64
82.47
82.18
83.17

71.61
71.43
70.92
71.26
71.52

Mesa 7: DM medoid factor ρDM = uhead offers
stable gains even without head information during
training. We tested ρDM = random five times.

ρDB = 0.5 y (0, 0, 0) shows the performance of
bare DB models.

On the flip side, DM’s (0, 0, 0) contains ran-
domness because of ρDM = random. We do not
intend to examine a static ρDM as DB yields nega-
tive results. Based on effective training tricks, el
variants enter the BO process at S2. DCCP shows
its sensitivity to ρ∅ in Table 6.

4.3 Inference with Unsupervised Headedness

Both CM and DM provide unsupervised head-
edness ¯λ. Chen et al. (2021) were unable to test
the benefits of CM’s unsupervised headedness
because it is a final product that cannot affect
analizando. Sin embargo, DM’s medoid affects parsing
actuación. On PLM DM, we select different
ρDM categories, which affect the location of all
discontinuous constituent, and examine their gen-
eralization on test sets, as shown in Table 7. Todo
models are trained with ρDM = random but infer-
ence with ρDM = uhead exerts positive gains on
exactitud.

Cifra 8: DB and DM’s discontinuity and multi-
branching performance. Because the TIGER Treebank
is richer in discontinuity, DM exhibits higher F1 scores.

5 Discusión

Properties of DCCP Models. Mesa 3 exhibits
high speeds and near state-of-the-art accuracies of
DCCP compared to recent works. DCCP inher-
its many properties from NCCP. CB and CM are
special cases of DB and DM without swap and dis-
continuous actions. All models contain compact
components without grammar restriction. Cada
model has no more than 4.7M parameters apart
from PWE or PLM, as listed in Table 2.

The variability of all models is low, excepto por
the discontinuous F1 score of PLM DB on DPTB,
as shown in Table 4. The main cause may not be
the random initialization but the different train-
ing processes of PWE and PLM models—PLM
models use the configuration of PWE models at
S2. Those degraded PLM models adopted low ρ∅
configuraciones (p.ej., ρ∅ = 0.078). Because DPTB
has less discontinuity and overall F1 scores are
used for model evaluation, high variability in
discontinuous F1 scores becomes more common
without several BO trials; this phenomenon is also
reflected in Table 5. DB lacks explicit disconti-
nuity, and the selection of hyperparameters seems
to be necessary on DPTB.

Cifra 8 presents the F1 scores for discontinu-
ity and multi-branching. We select PLM models

276

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Padre (#)

Head child by maximum weight

notario público (14.4k)
from CM

notario público(14.3k)
from DM

DT (4.5k); notario público (4.3k); NNP (1.6k);
JJ (922); NN (751); NNS (616);
etc.. (1.6k; 12 de 50 types with ‘‘*’’)

notario público (4.7k); DT (4.5k); NNP (1.6k);
JJ (786); NN (715); NNS (565);
etc.. (1.4k; 15 de 49 types with ‘‘*’’)

Mesa 8: CM & DM unsupervised NP headedness
de (D)PTB test sets. ‘‘*’’ denotes minority NPs
having DTs as non-head children (es decir., DTs are
strong NP heads).

seems to cascade to multi-branching prediction,
resulting in degradation of both properties. Significar-
mientras, DPTB is largely transformed from PTB
by typed traces and automatic rules, donde el
multi-branching accuracy stays more stable.

As seen in Table 7, ρDM = rightmost, leftmost
yielded the second best F1 and D.F1 scores on
DPTB and TIGER, respectivamente. The reversed
setting yielded poor results, even if not the worst.
The observation implies that many English heads
locate rightward (right-branching) and that Ger-
man heads tend to locate leftward (verb-second
word order, V2). German has abundant separable
verbs with their prefixes at the right-hand side of
the clauses.

Error Rates of DCCP. Greedy parsers allow
ill-formed outputs without a single root, especially
in case of single-model inference. Our models
yielded a few invalid parses, as demonstrated
en mesa 11. DM models produce more errors.
Sin embargo, unsuccessful decomposition of biaffine
attention matrices might not be the direct cause,
as also shown in Figure 11. ρ∅ > 0 variants
cleared the matrices that could not be decomposed
with any θ (FAIL). Similar to CM, this genre
suffers from more failures. Específicamente, the ply
size cannot be reduced to one during the iteration.
Greedy parsers must suffer the defect because
of their simplicity. Sin embargo, invalid parses can
contribute positive F1 scores and global parsers
can yield inaccurate parses.

We applied methods such as Boolean matrix
factorization and singular value decomposition.
Sin embargo, they did not provide any improvement
and significantly slowed down the speed. Esto es

Cifra 9: Beta distribution visualization for TIGER DB
at S2. See their hyperparameters in Figure 10.

whose overall F1 scores are close (es decir., most F1
differences are less than 0.1 and DB’s performance
es alto). DM exhibits persistent advantages over
DB when these properties are frequent. We fur-
ther determined that CM has the same gains over
CB starting identically from 4-ary nodes with mi-
nor score differences on (D)PTB under the same
ρ∅ = 0 condición, as shown in Table 9. El resultado
supports the argument of Xin et al. (2021), cual
asserts that m-ary constituency parsing without
binarization preserves some natural advantages,
p.ej., estructura del argumento predicado. Específicamente,
ρ∅ > 0 shifts DM’s multi-branching advantage
to frequent low-arity trees, favoring the overall
scores on DPTB, while it enhances both disconti-
nuity and multi-branching advantages on TIGER,
as shown in Table 10, in agreement with Table 6.
The training process of CB with CNF bina-
rization has a slight impact on parsing accuracy.
Chen et al. (2021) obtained the best CB with
Bernoulli distribution P (ρDB = 0) = 0.85 (es decir.,
PAG (ρDB = 1) = 0.15 or L85R15 in their for-
mat ‘‘L%R%’’) on PTB. They argued that such
binarization brings orientation balance.

Similarmente, DB’s S2 exhibits a slightly leftward
exploration with the beta distribution, as shown
En figura 9. DPTB has a similar situation. Todavía,
the optimized distributions are relatively uniform
and symmetric, which qualifies our uniform ran-
domness with ρDB at S1 and indicates a desired
property for future language-agnostic practice.

For unsupervised headedness, Chen et al.
(2021) reported that CM absolutely picks deter-
miners (DT) as heads for major NP. DM contin-
ues and further grammatically picks more noun
phrases (notario público) as NP heads, as shown in Table 8.

Linguistic Properties by DCCP. From Fig-
ura 8, we learned that TIGER is more challenging
in discontinuity. Incorrect discontinuity prediction

277

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Cifra 10: The BO process starts with S1 dev F1 scores (es decir., a small dot at each legend bottom) and ends with a
range of scores in S2. While the models are not sensitive to hyperparameters (p.ej., all gains are less than 0.54),
their preferences are different on respective corpora. On TIGER, αori < αshfl ori and high (βC, βX ) are preferable. Prop. M -ary 1 2 3 4 5 6 7 8 9 k > 1
Todo

Gold
Tree
9,073
26,338
7,009
1,490
344
96
32
12
3
731
44,397

PWE

ρ∅ = 0

ρ∅ > 0

PLM

ρ∅ = 0

ρ∅ > 0

CB
92.25
90.41
84.17
77.87
74.19
70.05
64.71
64.00
100.00
–
92.54

CM
92.02
89.94
83.56
78.95
77.29
78.35
86.15
72.73
75.00
–
92.08

DB
91.43
89.87
83.50
78.19
76.14
72.90
73.53
81.82
100.00
73.95
91.99

DM
91.35
89.68
83.34
79.82
78.46
80.63
87.10
85.71
75.00
77.60
92.02

DB
91.68
89.95
83.60
78.50
74.97
77.39
76.47
75.00
100.00
75.68
92.00

DM
91.53
90.02
83.79
78.86
78.87
76.29
71.43
80.00
50.00
78.94
92.00

CB
93.80
94.41
90.27
87.42
81.42
78.64
76.47
78.26
100.00
–
95.71

CM
94.33
94.33
89.81
86.51
84.06
80.00
70.18
86.96
75.00
–
95.44

DB
93.36
93.47
88.31
83.98
78.61
79.23
75.76
78.57
100.00
78.62
94.70

DM
93.41
93.65
88.60
86.50
85.15
83.50
77.42
83.33
85.71
83.04
94.79

DB
93.91
93.77
88.57
83.88
81.38
79.02
80.60
91.67
85.71
78.65
95.08

DM
93.82
93.92
88.87
85.90
83.17
76.44
54.90
63.16
100.00
82.71
95.09

Mesa 9: Multi-branching and discontinuous F1 scores of NCCP and DCCP on (D)PTB test sets. Nosotros
grouped k > 1 because only one tree has fan-out k = 2 in the test set. The scores of CB and CM are
from Chen et al. (2021).

Prop.
M -ary
1
2
3
4
5
6
7
8
9
k = 1
k = 2
k = 3
Todo

Gold
Tree
470
15,379
13,497
6,166
2,202
602
130
20
6
36,317
1,963
194
38,474

PWE ρ∅ = 0
DM
DB
49.14
45.37
82.57
81.83
80.58
80.41
73.43
73.34
64.14
63.66
52.85
50.72
43.38
36.25
24.39
11.24
66.60
12.90
85.95
85.92
59.88
59.64
59.83
57.46
84.56
84.50

ρ∅ > 0

DB
51.64
82.25
80.95
74.04
64.27
51.62
40.53
16.44
21.43
86.41
59.95
58.89
84.99

DM
55.32
83.36
81.05
74.36
65.15
55.13
44.91
19.51
28.57
86.39
62.05
60.61
85.08

PLM ρ∅ = 0
DM
DB
56.84
55.16
86.34
85.91
85.96
85.35
80.71
79.93
72.40
71.09
63.61
59.30
55.02
45.51
24.32
24.24
33.33
16.67
89.85
89.75
71.15
67.53
68.23
63.91
88.82
88.57

ρ∅ > 0

DB
54.45
86.50
86.93
81.76
73.37
61.32
43.59
16.67
12.90
90.69
69.78
69.21
89.55

DM
57.48
87.31
86.89
81.92
73.88
64.79
50.37
20.00
54.55
90.66
70.85
68.95
89.58

Mesa 10: Multi-branching and discontinuous test F1 scores of DCCP on TIGER. Fan-out is de-
tailed in k.

278

Test set

Total trees

DB’s ill-formed parses
DM’s ill-formed parses

Biaffine attention matrices

θ = 0.5 soluciones

Average of tries if θ (cid:12)= 0.5

FAIL + identity matrices

DPTB TIGER

2,416
1
15
594
587
12.4
0+4

4,998
2
47
7,278
7,114
42.9
0+81

Mesa 11: Errors in DCCP PLM models with
ρ∅ > 0. A FAIL causes a matrix of ones, mientras
a θ close to one yields an identity matrix—an
expensive null action.

Cifra 11: The numbers of tries to decompose biaffine
attention matrices on test sets. ‘‘(cid:4)’’ marks FAILs.

Cifra 12: Signal polarity in corpora DPTB (secciones
2–24) and TIGER. Top: DB signal polarity to ρDB
with orientation right ‘‘•’’ and joint ‘‘(cid:4)’’ Bottom:
DM signal polarity to stratifying medoid factor ρDM
with affinity ‘‘•,’’ joint ‘‘(cid:4)’’ and discontinuity ‘‘ .’’
Continuous and head are referential only, looking for
the least discontinuity and leveraging head information.
(All ρ∅ = 0.)

279

Cifra 13: An exact matched DPTB sample from PLM
DB and DM models versus CM on PTB. The parse
contains complex nested clauses that CM must fail
to capture, and it becomes ungrammatical in the con-
tinuous scenario. DB’s outputs include orientations
depicted as arrows and their traveling traces colored
for groups. Mientras tanto, DM produces two biaffine
attention matrices, one of which has a highly biased
but correct threshold θ = 0.99. Bar heights indicate
values in matrices and their colors indicate the
relationship to θ.

because θ (cid:12)= 0.5 cases are few. Nuestro
se-
quential tries to decompose might be na¨ıve, pero
they are effective. En figura 11, the number of
tries does not significantly increase under θ < 0.9 within 50 tries. For θ ≥ 0.9, although some tries are expensive, we will see that they are worthy in the next section. The imbalanced signals from both datasets account for the bias of θ—more than 92% of the biaffine attention signals are ones, as shown in Figure 12. All affinity biases (i.e., baff ∈ [−1.60, −0.84]) are significant negative numbers for counteraction. Weakness. The design of affinity as biaffine attention is an initial but coarse attempt, which l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / t a c l / l a r t i c e - p d f / d o i / . 1 0 1 1 6 2 / t l a c _ a _ 0 0 5 4 6 2 0 7 5 7 3 9 / / t l a c _ a _ 0 0 5 4 6 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / t a c l / l a r t i c e - p d f / d o i / . 1 0 1 1 6 2 / t l a c _ a _ 0 0 5 4 6 2 0 7 5 7 3 9 / / t l a c _ a _ 0 0 5 4 6 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Figure 14: A TIGER parse. DB natively with ∅-subtrees achieved the exact match but DM erred with ρ∅ = 0. brings imbalance. If one encoded dependency within constituent to biaffine attention instead, both signal balance and multi-grammar parsing might be better addressed. However, based on the context of constituency parsing, we leave this topic for future study. 6 Sample Analysis Continuous vs. Discontinuous Parsing. Fig- ure 13 highlights the value of discontinuous pars- ing by demonstrating the respective CM parse. Conspicuously, the branching tendency of the continuous parse is to the right, while it is not obvi- ous for the discontinuous parsing. Meanwhile, we observed instances of similar unsupervised head- edness weights. This sample is not trivial, which challenges our PLM DCCP models. In Figure 13, DB Parsing Process of DCCP. shows sinuous travel traces of ‘‘I,’’ ‘‘was,’’ and larger ∅-subtree nodes that involve the turning of orientations. The varying context leads them to achieve complex movement. DB also cre- ated some grammatical substructures for ‘‘How,’’ ‘‘referred,’’ ‘‘to,’’ ‘‘was,’’ ‘‘in,’’ and ‘‘school.’’ Meanwhile, the DM parse is more dramatic. The formation of the lower discontinuous VP involves five nodes, two of which are irrelevant words ‘‘How’’ and ‘‘referred’’ triggered by incorrect discontinuity signals. They are discontinuous but for the higher VP. The two nodes create a noisy bi- affine attention matrix because their grammatical roles are compatible with the lower VP. Trained for extra robustness, the matrix decomposition with five tries found the right θ to identify the correct VP members excluding ‘‘How’’ and ‘‘referred.’’ The interply loss and the decoding process gave this parse a chance for perfection. In Figure 14 for German, DB achieved a long distant constituent in a more subtle way. The word ‘‘zwar’’ joins ‘‘registriert’’ as an ∅-subtree when the formation of intermediate NP shortens their distance and prevents a travel through. ‘‘Gegenw¨artig’’ follows and forms a VP. However, DM failed. The ρ∅ > 0 matters. The above failure ex-
plains why DM is inferior to DB with ρ∅ = 0.
DB’s orientation system allows some free travel
before joining with correct mates. The constituent
formation through steps of accumulation creates

280

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Maximin Coavoux, Benoˆıt Crabb´e, and Shay B.
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Cifra 15: A semantic ∅-subtree by DM with ρ∅ > 0.
Copula ‘‘were’’ has less affinity than ‘‘when’’ and
‘‘due.’’

a more stable context. Sin embargo, DM’s group ac-
tion happens all at once. An incorrect composition
might create a quite different context, que lleva
to unseen reaction chains. The strange unsuper-
vised headedness weights reflect the issue. Sobre el
other side, with ρ∅ > 0, DM can also gradually
build and discover some semantic substructures,
as shown in Figure 15. A diferencia de, DB is not
sensitive to ρ∅ because of its nature, in agreement
with Table 6.

7 Conclusión

We proposed a pair of efficient and effective
discontinuous combinatory constituency parsers
and extended the neural combinator family of
NCCP. A binary combinator DB is based on
orientation extended into a joint-swap system.
A multi-branching combinator DM leverages bi-
affine attention adapted for constituency. Nuestro
modelos (como en la tabla 2) achieved state-of-the-
art discontinuous F1 scores with a significant
advantage in speed.

En el futuro, we will aim to extend DCCP
into a multilingual tool for directed acyclic graph
(DAG) parsing with function tag prediction for
estructura del argumento predicado.

Expresiones de gratitud

We extend special thanks to our action editor,
anonymous reviewers, and Prof. Yusuke Miyao
for their invaluable comments and suggestions.
This work was partly supported by TMU research
fund for young scientists.

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