CoRec: An Easy Approach for Coordination Recognition
Authors: Qing Wang
Haojie Jia
Wenfei Song
Qi Li
Venue: EMNLP 2023 Main (2023)
Keywords: coordination recognition
shallow parsing
TL;DR: A pipeline model designed for coordination recognition task without using syntactic parsers.

Abstract: In this paper, we observe and address the challenges of the coordination recognition task. Most existing methods rely on syntactic parsers to identify the coordinators in a sentence and detect the coordination boundaries. However, state-of-the-art syntactic parsers are slow and suffer from errors, especially for long and complicated sentences. To better solve the problems, we propose a pipeline model COordination RECognizer (CoRec). It consists of two components: coordinator identifier and conjunct boundary detector. The experimental results on datasets from various domains demonstrate the effectiveness and efficiency of the proposed method. Further experiments show that CoRec positively impacts downstream tasks, improving the yield of state-of-the-art Open IE models.

CoRec: An Easy Approach for Coordination Recognition
Qing Wang, Haojie Jia, Wenfei Song, Qi Department of Computer Science, Iowa State University, Ames, Iowa, USA (qingwang, hjjia, wsong, qlijeiastate. edu
Abstract
It is a common practice to apply constituency parsers or dependency parsers identify the coor- dination structures of a sentence. However, there are several drawbacks. First, the state-of-the-art syntactic parsers confront an increase of errors when processing conjunctive sentences, especially when the input sentence contains complex coordi- nation structures. Second, applying parsers be slow, which will make the identification of coordi- nation less efficient. Existing coordination bound- ary detection methods rely on the results of syn- tactic parsers (Ficler and Goldberg, 2016, 2017; Saha and Mausam, 2018) and thus still face similar drawbacks.
this paper, observe and address the chal- lenges of the coordination recognition task. Most existing methods rely on syntactic parsers identify the coordinators in sentence and detect the coordination boundaries. However, state-of-the-art syntactic parsers are slow and suffer from erros, especially for long and com- plicated sentences. To better solve the prob- lems, we propose a pipeline model COordina- tion RECognizer (CoRec). It consists of two components: coordinator identifier and con- junet boundary detector. The experimental re- sults on datasets from various domains demon- strate the effectiveness and efficiency of the pro- posed method. Further experiments show that CoRec positively impacts downstream tasks, improving the yield of state-of sfateoof-the-ar art Open IE models. Source code is available'.
In this work, we approach the coordina- tion recognition problem without using syntactic parsers and propose a simple yet effective pipeline model COordination RECognizer (CoRec). CoRec composes of two steps: coordinator identification and conjunct boundary detection. For coordinator identification, consider three types of coordina- spans: contiguous span coordinators (e.g. "or' and 'as well as") paired span coordinators (e.g. 'ei- the....or...".. and coordination with 'respectively'. Given each identified coordinator span, we formu- late the conjunct boundary detection task as a se- quence labeling task and design a position-aware BIOC labeling schema based on the unique char- acteristics of this task. We also present simple trick called coordinator markers that can greatly improve the model performance.
1 Introduction
Coordination is a widely observed syntactic phc- nomenon in sentences across diverse corpora. Based on our counting, 39.4% of the sentences in OntoNotes Release 5.0 (Weischedel et al., 2013) contain at least one coordination. The frequently appeared conjunctive sentences bring many chal- lenges various NLP tasks, including Natural Lan- guage Inference (NLI) (Saha al., 2020), Named Entity Recognition (NER) (Dai et al., 2020), and text simplification (Xu et al., 2015). Specifically, in Open Information Extraction (Open IE) tasks, researchers find that ineffective processing of con- junctive sentences will result in substantial yield lost (Corro and Gemulla, 2013; Saha and Mausam, 2018; Kolluru al., 2020), where yield is essential since Open IE tasks aim to obtain a comprehen- sive set of structured information. Thus processing conjunctive sentences is important to improve the performance of Open IE models.
Despite CoRec's simplicity, we find it to be both effective and efficient in the empirical stud- ies: CoRec consistently outperforms state-of-the- art models benchmark datasets from both gen- eral domain and biomedical domain. Further exper- iments demonstrate that processing the conjunctive sentences with CoRec can enhance the yield Open IE models.
In summary, main contributions
1tttps: http://ggttttttt/e// com/ http://innnnniiu/eeee isu/CoRec
We propose a pipeline model CoRec, a special-

---

ized coordination recognizer without using syntac- tic parsers.
from S. The conjunct boundary detector takes the positions of all the potential target coordinator spans as additional input and detects the conjuncts coordinated by each target coordinator span.
We formulate the conjunct boundary detec- tion task as a sequence labeling task with position- aware labeling schema.
3.2 Label Formulation
Empirical studies on three benchmark datasets from various domains demonstrate the efficiency and effectiveness of CoRec, and its impact yield of Open IE models.
Since the coordinator spans are usually short, we adopt simple binary labels for the coordinator iddn- tification sub-task: label "C" for tokens inside coor- dinator spans and "O' for all other tokens.
2 Related Work
For the conjunct boundary dctection sub-task, conjuncts can be long and more complicated. Thus formulate this sub-task as sequence labeling task. Specifically, inspired by the BIO (Beginning- Insidc-Outside) (Rumshaw and Marcus, 1995) la- beling schema of the NER task, we also design position- aware labeling schema, as previous re- searches have shown that using a more expressive labeling schema can improve model performance (Ratinov and Roth, 2009; Dai et al., 2015).
For the tasks of coordination boundary detection and disambiguation, earlier heuristic, non-learning- based approaches design different types of features and principles based on syntactic and lexical anal- ysis (Hogan, 2007; Shimbo and Hara, 2007; Hara al., 2009; Hanamoto et al., 2012; Corro and Gemulla, 2013). Ficler and Goldberg (2016) are the first to propose neural-networkk for coordination boundary detection. This model operates on top of the constituency parse trees, and decomposes the trees to capture the syntactic con- text of each word. Teranishi et al. (2017, 2019) design similarity and replaceability feature vectors and train scoring models to evaluate the possible boundary pairs of the conjuncts. Since these meth- ods are designed to work on conjunct pairs, they have natural shortcomings to handle more than two conjuncts in one coordination.
model
The proposed labeling schema contains both po- sition information for each conjunct and position information for each coordination. For each con- junct, we use 'B' to label the beginning token and 'I' to label the following tokens. For each coordi- nation structure, we further append 'before' and 'after' tags to indicate the relative positions to the target coordinator, which is/are labeled 'C'.
3.3 Labeling Details
Researchers in the Open Information Extraction domain also consider coordination analysis to be important to improve model performance. CALM, proposed by Saha and Mausam (2018), improves upon the conjuncts identified from dependency parsers. It ranks conjunet spans based on the 're- placeability" principle and uses various linguistic constraints to additionally restrict the search space. 0pen1E6 (Kolluru et al., 2020) also has coordi- nation analyzer called IGL-CA, which utilizes a novel iterative labeling-basee architecture. How- ever, its labels only focus on the boundaries of the whole coordination and do not utilize the position information of the specific conjuncts.
In this section, categorize the coordinator spans into three types and use simple examples to show the labeling details of each type.
Contiguous Span Coordinators Processing con- tiguous span coordinator is straightforward. Take the sentence "My sister likes apples, pears, and grapes." as an example, following Section 3.2 we should generate one instance with labels as shown in Figure (1).
Paired Span Coordinators Each paired span co- ordinator consists of two coordinator spans: the left coordinator span, which appears at the beginning of the coordination, and the right coordinator span, which appears in the middle. The right coordinator span stays more connected with the conjuncts due to relative position. Therefore, choose to detect the conjuncts only when targeting the right coordi- nator span. Take the sentence "She can have either green tea or hot chocolate." " as an example, follow- ing Section 3.2 we should generate two instances with labels as shown in Figure (2).
3 Methodology
3.1
Task Formulation
Given sentence S (21,..., kn), we decom- pose the coordination recognition task into two sub-tasks, coordinator identification and conjunet boundary detection. The coordinator identifier aims to detect all potential target coordinator spans

---

(1)
My
sister
likes
upples
peurs
[C]
und [C]
gripes
B-before
1-tffrr
B-before
Ibefom
B-after
She
have
[C]
either
[C]
green
tea
hot
chocolate
C
0
0
0
o
She can have
either
green
[C]
or
hot chocolate
B-before
I-before
B-atter I-ater
The dog [C]
and [C]
the
were named Jack and
Sam
respectively
R-hefore Lihafore
C
R-afta
T-after
0
0
0
0 respectively
The dog and
named
Jack
and
[C]
Sam
(3)
B-before
C
B after
The dog
und
the
cat
were named
Jack
and
Sam
[C]
respoctively
C
B-before I-before
B-after Fafter
B-befone
B-after
That should
stop
lot
account
churning
[C)
and
[C]
produce
B-before
I-before I-before
H-bffo
I-before
I-bffo
c
c
B-after
L-ffeer
stock market driven
only
by
professional
concern
cureful
thought and
good
sense
(4)
F-ther
E-after Tafeer
T-aftrr
Bafter
I-afler
T-after
T-fler
I-after I-after
I-after
T-afler
That should stop
lot.
account
chuminng
and produce
slock
market
0
D
0
Q
0
O
driven
only by
professional concern
careful
thought
[C]
and
[C]
good
sense
B-before
l-before
B-before
I-before
B-after
Figure 1: Labeling Examples. (1): Conjunctive sentence labeling with contiguous span coordinator 'and"; (2): Conjunctive sentence labeling with paired span coordinator 'either...or...'' (3): Conjunctive sentence laheling with 'respectively"; (4): Nested conjunctive sentence labeling.
Coordination with 'Respectively' The conjunc- tive sentence with 'respectively' usually has the where the first and the second coordination have the same number of conjuncts.
A linear projection layer is then added. We de- note coordinator spans detected by the coordinator identifier as P = {11 P2; .... Pk}.
structure ..a.ndd.andd..rsspcttiiie
3.5 Conjunct Boundary Detector
The 'respectively" case is handled differently training and inference. During training, for a a...dd..ndd..rrspeetteee..
The conjunct boundary detector then processes each target coordinator span E P independently to find all coordinated conjuncts in sentence S. To inject the target coordinator span information into the encoder, we insert coordinator markers, '[CJ' token, before and after the target coordina- tor span, respectively. The resulting sequence is Sm = { [C], (C]....m.): For simplicity we denote Sm. [11 wm)-
sentence, we recog- nize three coordinator spans ('and', 'and', and 're- spectively') ) and generate three training instances with different target coordinator spans. Take the sentence "The dog and the cal were named Jack and Sam respectively." as an example, we should generate three instances with labels as shown in Figure 1 (3). This is because 'and' is one of the most common coordinators and occurs much more frequently than "..and... an..ddd..nn....sspcciie...... If we only consider "respectively" as the sole targer co- ordinator in the sentence and do not consider 'and' coordinator during training, the model can be confused. During inference, when encountering a sentence containing 'respectively', we consider the conjuncts recognized when 'respectively' the target coordinator span as the final result.
The marked sequence Sm is fed into a BERT encoder:
[hg hodd Ency ([w wml)). (2)
The position information of all the coordinators found by the coordinator identifier can help the model to understand the sentence structure. Thus we encode such information into vector b; to indicate if w; is part of detected coordinator span. Given wy E Sm. we concatenate its en- coder output and coordinator position encoding as hら = [hghd, bi).
3.4 Coordinator Identifier
Finally, we use CRF (Lafferty al., 2001) layer to ensure the constraints on the sequential rules of labels and decode the best path all possi- ble label paths.
As mentioned above, the coordinator identification sub-task is formulated as a binary classification problem. Our coordinator identifier uses BERT (Devlin et al., 2019) encoder to encode sentence S (I1, F2, .... }. and the output is:
3.6 Training & Inference
The coordinator identifier and the conjunct bound- ary detector are trained using task-specific losses.
[hi h = ([nc)(21, xn]).

---

For both, we fine-tune the two pre-trained BERT base encoders. Specifically, we use cross- entropy loss:
Testing Setup We use three testing datasets evaluate the performance of the proposed CoRec model. The first dataset, ontoNotes, contains 1,000 randomly selected conjunctive sentences from the English portion of OntoNotes Release 5.0² (Weischedel et al., 2013). The second dataset, Genia, contains 802 conjunctive sentences from the testing set of GENIA* (Ohta et al., 2002), benchmark dataset from biomedical domain. The third dataset, Penn, contains 1,625 conjunctive sen- tences from Penn Treehank testing set (WSJ 22-24). These three datasets contain the gold standard con- stituency parsing annotations. We convert them into the OC and BIOC labels in the same way as described in Section 4.
Le = Σ
log logPo(17(00.)
2,ES
Libd Σ
Pog (2
(4)
where ti, 2 represent the ground truth labels. Dur- ing inference. we first apply the coordinator identi- fier and obtain:
yo(x;) = argmax Po(4)a0))
(5)
G,T
Then we use its prediction ye(z,) with the original sentence as input to the conjunct boundary detector and obtain:
Each testing dataset is further split into a 'Sim- ple' set and a 'Complex' set based on the complex- ity of the coordinators. 'Simple" set contains in- stances with 'and', 'but', "or" target coordinators only and these three coordinators can be handled by all baselines. Whereas the 'Complex' set contains other scenarios including multi-token contiguous span coordinators (e.g. "as well as'), the paired span coordinators (c.g.. 'not only...but also... and coordination with 'respectively'. 'Complex' in- stances may not be hard to predict. For example, the instances with paired span coordinators may be casier for the model since the first coordinator span may give better clues about the boundaries of conjunets. Table provides the respective counts of instances in 'Simple' and 'Complex' sets for three testing datasets.
y argmax Paba[[21,.... =m]l [w1, Wm)). 3m
(6)
where T' and Z represent the set of possible labels of each model respectively.
3.7 Data Augmentation
We further automatically augment the training data, The new sentences are generated following the sym- metry rule, by switching the first and last conjuncts of each original training sentence. Since all sen- tences are augmented once, the new data distri- bution only slightly differs from the original one, which will not lead to a deterioration in perfor- mance (Xie al. 2020).
4 Experiments
Nntos
Genia
Penn
Training Setup The proposed CoRec is trained on the training set (WSJ 0-18) of Penn Treebank2 (Marcus et al., 1993) following the most common split, and WSJ 19-21 are used for validation and WSJ 22-24 for testing. The ground truth con- stituency parse trees containing coordination struc- tures are pre-prot proppecessed to generate labels for the two sub-tasks follows. If constituent is tagged with 'CC' or 'CONJP', then it is considered a co- ordinator span. For each coordinator span, we first extract the constituents which are siblings to the coordinator span, and each constituent is regarded as a conjunct coordinated by that coordinator span. We automatically generate labels as described in Section 3.2. We also manually check and correct labels for complicated cases.
Simple Complex
1123
2193
1981
127
327
267
Table The statistics 'Simple' and 'Complex" sets
three testing datasets.
Baseline Methods We compare the proposed CoRec with two categories of baseline meth- ods: parsing-based and learning-based methods. Parsing-based methods convert the constituency parsing results and regard constituents at the same depth with the target coordinator spans as coordi- nated conjuncts. We adopt two state-of-the-art con- stituency parsers, AllenNLP (Joshi et al., 2018 88) and
'tttps://catalog wdw..penn.......ommmmm upenn. edw.eLL22L/ http:///ww.geniaproont wwtpaapproooeooeeeeeeeeeeeseeeee wrtpeeniaaag//aaaaaooaoooa corpus/ treebank
"https://catalln upe.unnnnnnnn.....mmmmmmmmm
1dc. www.n.eeenn.m. Biieennedu/LDCC9TA.

---

ontoNotes (Simple)
Genia (Simple)
Penn (Simple)
Model
R
F1
Time
R
F1
Time
P
F1
Time
AllenNLP
74.2
68.4
71.2
452s
79.7
47.7
59.7
1059s
88.7
67.7
76.8
823s
Stanford
56.9
53.4
55.1
763s
73.8
72.2
73.0
1722s
81.8
79.3
80.6
1387s
Teranishi+19 19
68.3
60.8
64.7
167s
76.4
65.2
70.3
136 s
74.2
75.5
75.4
217s
IGL-CA
77.6
59.7
67.5
17s
78.0
64.3
71.0
27s
87.9
86,9
87.4
17s
CoRec (our)
72.4
75.8
74.1
32s
82.0
81.2
81.6
15s
88.3
89.2
88.8
57s
ontoNotes (Complex)
Genia (Complex)
Penn
(Complex)
AllenNLP
84.6
49.4
62.4
10Ss
82.3
25.7
39.2
370s
90.2
61.7
73.2
363 s
Stanford
62.4
34.5
44.4
248
64.3
32.9
43.5
831s
86.1
69.7
77.1
530
CoRec (our)
73.1
79.3
76.0
48
67.7
56.7
61.7
9s
91.5
89.5
90.5
10s
Table 2: Performance Comparison (average over 5 runs)
Stanford CoreNLP parrerss for this category. For learning-bas methods, we choose two state-of- the-art models for coordination boundary detection, Teranishi+ 19 (Teranishi et al., 2019), and IGL-CA (Kolluru et al., 2020). All results are obtained using their official released code.
Model Stanford
Before
After
Yield
12210
21284 12085
+74.3%
Open1E6
8084
+58.4%
Table 3: The impact of CoRec Open IE Yield
Model
Precision
Recall
Fl
BERT
78.73
85,46
81.96
+[C] mark
87.15
88.92
88,03
We evaluate both the effec- tiveness and efficiency of different methods. We evaluate effectiveness using span level precision, recall, and F1 scores. A predicted span is correct only if it is an exact match of the corresponding span in the ground truth.
Evaluation Metries
+CRF
88.36
89.35
+aug 89.28
90.21
89.74
Table 4: Ablation Study
Sentence splitting significantly increases the yield of unique extractions for both models. Though Open1E6 implements the coordination boundary detection method IGL-CA, the coordination struc- ture still negatively impacts the Open IE yield.
For efficiency evaluation, we report the infer- ence time of each method. All experiments are conducted on computer with Intel(R) Core(TM) 17-11700k 3.60GHz CPU, NVIDIA(R) RTX(TM) 3070 GPU, and 40GB memory.
4.3 Ablation Study
We perform an ablation study to assess the con- tribution of different components to performance. The base model only uses BERT encoder, then co- ordinator markers, CRF, and data augmentation are incrementally added. The testing results on Penn dataset are shown in Table 4. From the results, we can see that all of the components can improve the performance in terms of precision and recall.
4.1 Main Results
The results are shown in Table In terms of effectiveness, CoRec's recall and Fl scores are higher than all baselines on all datasets, and the improvement on FI scores is 2.9, 8.6, and 1.4 for ontoNotes, Genia, and Penn compared to the best baseline methods, respectively. Although CoRec is not trained on biomedical corpus, it still demon- strates superior performance. The inference time of CoRec is also competitive.
Conclusions
In this paper, we develop CoRec, a simple yet ef- fective coordination recognizer without using syn- tactic parsers. We approach the problem by formu- lating a pipeline of coordinator identification and conjunct boundary detection. CoRec can not only detect the boundaries of more than two coordinated conjuncts but also handle multiple coordinations in one sentence. It can also deal with both simple and complex cases of coordination. Experiments show that CoRec outperforms state-of-the-at art mod- els on datasets from various domains. Further ex- periments imply that CoRec can improve the yield of state-of-the the-trr Open IE models.
4.2 Impact of CoRec on Open IE Tasks
To show the impact of CoRec on downstream tasks, we implement a sentence splitter that generates sim- plc, non-conjunctive sub-sentences from CoRec's output. We apply two the--ar Open IE mod- els, Stanford OpenlE (Angeli et al., 2015) and Ope- nIE6 (Kolluru et al., 2020), to extract unique rela- tion triples on the Penn dataset before and after our sentence splitting. The results shown in Table 3.
'https://nlp stw.sstaaffaeo stanford, edu/ sof wttp////aaarreerrrrrrr.
html

---

Limitations
Xiang Dai, Sarvnaz Karimi, Ben Hachey, and Cécile Paris. 2020. An effective transition based model for discontinuous NER. In Proceedings of the 58th An- nual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pages 5860-5870. Association for Computational Linguisties.
Language Limitation The proposed CoRec model works mostly for languages with limited morphology, like English. Our conclusions may not be generalized to all languages.
Label Quality Limitation We use ground truth constitenncy parse trees from Penn Treebank, GE- NIA, and OntoNotes Release 5.0 (Marcus et al., 1993; Weischedel et al., 2013; Ohta et al., 2002) to generate the labels. Since the parsing does not tar- get for the coordination recognition task, we apply rules for the conversion. A single author inspected the labels for complicated cases but did not inspect all the labels. There could be erroneous labels in the training and testing data.
Jacob Devlin, Ming-Wei Wei Chang, Kenton Lee, and Kristina Toutamova. 2019. BERT: pre-training of deep bidirectional transformers for language under- standing. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguisties: Human Language Tech- nologies, NAACL-HLT 2019, Minneapolis, MN. USA, June 2-7, 2019, Volume (Long and Short Papers), pages 4171-4186. Association for Computational Linguistics.
Jessica Fieler and Yoav Goldberg. 2016. A neural net- work for coordination boundary prediction. In Pro- ceedings of the 2016 Conference on Empirical Meth- ods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, November 1-4, 2016, pages 23- 32. The Association for Computational Linguisties.
Comparison Limitation Comparison to the parsing- based methods may not be precise. Parsers are not specialized in the coordination recognition task. Our task and datasets may not be the best fit for their models.
Jessica Ficler and Yoav Goldberg. 2017. Improving a strong neural parser with conjunction-specitii fea- tures. In Proceedings of the 15th Conference of the European Chapter of the Association for Computa- tional Linguistics, EACL 2017, Valencia. Spuin, April 3-7, 2017, Volume 2: Short Papers, pages 343-348. Association for Computational Linguisties.
Ethics Statement
We comply with the EMNLP Code of Ethics.
Acknowledgments
The work was supported in part by the US National Science Foundation under grant NSF-CAREER 2237831. We also want to thank the anonymous reviewers for their helpful comments.
Atsushi Hanamoto, Takuya Matsuzaki. and Jun anhi Tsu- jüi. 2012. Coordination structure analysis using dual decomposition. In EACL 2012, 13th Conference of the European Chapter of the Association for Com- putational Linguistics, Avignon, France, April 23-27, 2012, pages 430-438. The Association for Computer Linguistics.
References
Kazuo Hara, Masashi Shimbo, Hideharu Okuma, and Yuji Matsumoto. 2009. Coordinate structure analy- sis with global structural constraints and alignment- based local features. In ACL 2009, Proceedings of the 47th Annual Meeting of the Association for Com- putational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLP, 2-7 August 2009, Singapore, pages 967-975. The Association for Computer Linguistics.
Gabor Angeli, Melvin Jose Johnson Premkumar, and Christopher D. Manning. 2015. Leveraging linguis- tie structure for open domain information extraction. In Proceedings of the 53rd Anmual Meeting of the Association for Computational Linguistics and the
7th International Joint Conference on Natural Lan- guage Processing of the Asian Federation of Natural Language Processing, ACL 2015, July 26-31, 2015, Beijing, China, Volume 1: Long Papers, pages 344- 354. The Association for Computer Linguistics.
Deirdre Hogan. 2007. Coordinate noun phrase disam- biguation in a generative parsing model. In ACL 2007, Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, June 23- 30, 2007, Prague, Czech Republic. The Association for Computational Linguistics.
Luciano Del Corro and Rainer Gemulla. 2013. Clausie: clause-based open information extraction. In 22nd International World Wide Web Conference, WWW 113 Rio de Janeiro, Brazil, May 13-17, 2013, pages 355-366. International World Wide Web Conferences Steering Committee/ / ACM.
Vidur Joshi, Matthew E. Peters, and Mark Hopkins. 2018. Extending a parser to distant domains using a few dozen partially annotated examples. In Proceed- ings of the 56th Annual Meeting of the Association for Computational Linguisties, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers.
Hong-Jie Dai, Po-Ting Lai, Yung-Chun Chang, and Richard Tzong- Tsai. 2015. Enhancing of chem- ical compound and drug name recognition using rep- resentative tag scheme and fine-grained tokenization. J. Cheminformatics, 1(541):

---

pages 1190-1199. Association for Computational Linguistics.
Conference on Empirical Methods in Natural Lan- guage Processing and Computational Natural Lan- guage Learning, June 28-30, 2007, Prague, Czech Republic, pages 610-619, ACL.
Keshav Kolluru, Vaibhav Adlakha, Samarth Aggarwal, Mausam, and Soumen Chakrabarti. 2020. 0penie6: Iterative grid labeling and coordination analysis for open information extraction. In Proceedings of the 2020 Conference on Empirieal Methods in Natural Language Processing, EMNLP 2020, Online, Novem- ber 16-20, 2020, pages 3748-378 3761.. Association for Computational Linguistics.
Hiroki Teramishi, Hiroyuki Shindo, and Yuji Matsumoto. 2017. Coordination boundary identification with similarity and replaceability. In Proceedings of the Eighth International Joint Conference on Natural Longuage Processing, IJCNLP 2017, Taipei. Taiwan, November 27 December 1, 2017 - Volume I: Long Papers. pages 264-272. Asian Federation of Natural Language Processing.
John Lafferty, Andrew McCallum, and Fernando C. N. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling se- quence data, In Proceedings of the Eighteenth In- ternational Conference on Machine Learning (ICMI. 2001). Williams College, Williamstown, MA, USA, June 28- July 2001, pages 282 289. Morgan Kauf- mann.
Hiroki Teranishi, Hiroyuki Shindo, and Yuji Matsumoto. 2019. Decomposed local models for coordinate struc- ture parsing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, NAACL-HLD 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume (Long and Short Papers), pages 3394-3403. Association for Computational Linguisties.
Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. 1993. Building large annotated corpus of english: The penn treebank. Comput. Lin- guistics, 13-330.
Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Ni- anwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-B El-Bachouti, Robert Belvin, and Ann Houston. 2013. OntoNotes Release 5.0.
Tomoko Ohta, Yuka Tateisi, and Jin-Dong Kim. 2002. The genia corpus: An annotated research abstract cor- pus in molecular biology domain. In Proceedings of the Second International Conference Human Lan- guage Technology Research, HLT '02. page 82 22-86. San Francisco, CA, USA. Morgan Kaufimann Pub- lishers Inc.
Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, and Quoe V. Le. 2020. Adversar- examples improve image recognition. IEEF/CVE Conference on Computer Vision and Pat- tern Recognition. CVPR 2020, Seattle, WA, USA, June 13-19, 2020, pages 816-825. Computer Vision Foundation / IEEE.
2020
Lance A. Ramshaw and Mitch Marcus. 1995. Text chunking using transformation-basee learning. In Third Workshop Very Large Corpora, VLC@ACL 1995, Cambridge, Massachusetts, USA, June 30, 1995.
Wei Xu, Chris Callison- Callison-Burch, and Courtney Napoles. 2015. Problems in current text simplification search: New data can help. Trans. Assoc. Comput. Linguistics, 3:283-297.
Lev-Arie Ratinov and Dan Roth. 2009, Design chal- lenges and misconceptions in named entity recogni- tion. In Proceedings of the Thirteenth Conference on Computational Natural Language Learning, CoNLI 2009, Boulder, Colorado, USA, June 4-5, 2009, pages 147-155. ACL.
Swarnadeep Saha and Mausam. 2018. Open informa- tion extraction from conjunctive sentences. In Pro- ceedings of the 27th International Conference on Computational Linguistics, COLJNG 2018, Santa Fe, New Mexico, USA, August 20-26, 2018, pages 2288- 2299. Association for Computational Linguistics.
Swarnadeep Saha, Yixin Nie. and Mohit Bansal. 2020. Conjnli: Natural language inference over conjunctive sentences. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16- (6-20, 2020, pages 8240-8252, Association for Computational Linguis- tics.
Masashi Shimbo and Kazuo Hara. 2007. A discrimina- tive learning model for coordinate conjunctions. In EMNLP-CONILL 2007, Proceedings of the 2007 Joint

---

A Error Analysis
To better understand the bottleneck of CoRec, we conduct case study to investigate the errors that CoRec makes. We randomly selected 50 wrong predictions and analyzed their reasons. We identify four major types of errors as follows (for detailed examples check Table 5):
Ambiguous Boundaries (38%) The majority of the errors occurred when the detected boundaries are ambiguous. In this case, although our predic- tion is different from the gold standard result, they can both be treated as true. We identify two com- mon types of ambiguous boundaries: ambiguous shared heads (28%) and ambiguous shared tails (10%). The former usually signaled by 'a/an/the' and shared modifiers. The latter usually signaled by prepositional phrases.
Errors without Obvious Reasons (32%) Many errors occurred without obvious reasons. However, we observe that CoRec makes more mistakes when the original sentences contain large amount of certain symbols (e.g.." '?).
Wrong Number of Conjuncts (22%) Some- times CoRec detects most conjuncts in the gold standard set but misses a few conjuncts. In some other cases, CoRec would detect additional con- junets to the correct conjuncts.
Low-Quality Gold Labels (8% We find there may also be some low-quality ground truth parse trees, thus generating incorrect gold labels. In this case CoRec may make a correct prediction that is different from the ground truth.

---

Category
Ground Truth
CoRec
Ambiguous Boundaries
I'm not going to worry about one day's decline, said Kenneth Olsen, digital equipment corp. president, who was leisurely strolling through the bright [or- ange] and [yellow] leaves of the mountains here after his com- pany's 's shares plunged $5.75 to close at $86.5.
I'm not going to worry about one day's deeline, said Kenneth Olsen, digital equipment corp. president, who was leisurely strolling through [the bright or- amge] and [yellow] leaves of the mountains here after his com- pany's 's shares plunged $5.75 to close at S86.5.
Errors without Obvious Reasons
For example, the delay in sell- ing people's heritage savings, Salina Kan, with $1.7 billion in assets, has forced the RTC to consider selling off the thrift brrach-byy-ranchh instead of [as a whole institution].
For example, the delay in sell- ing people's heritage savings, Salina Kan, with $1.7 billion in assets, has forced the RTC to consider [selling off the thrift branch-by-branchh, instead of [as whole institution].
Wrong Number of Conjuncts
Sales of Pfizer's important drugs, [Feldene for treating arthritis.] and [Procardia, heart medicine], have shrunk because of increased competition.
Sales of [Pfizer's important drugs,] [Feldene for treating arthritis.] and [Procardia, heart medicine], have shrunk because of increased competition.
Low-Quality Gold Labels
The executives were remarkably unperturbed by the plunge even though it [lopped billions of dol- lars off the value of their com- panies] and [millions off their personal fortunes].
The executives were remarkably unperturbed by the plunge even though it lopped [billions of dol- lars off the value of their com-
panies] and [millions off their personal fortunes].
Table 5: Case study conjunct boundary detection results on Penn dataset. For each case, the ground truth conjuncts are colored red and the CoRee detected conjuncts are colored blue.


Reviews:
[{"review_id": "4TPTcUjH27", "reviewer": "Reviewer_4paW", "summary": "", "questions": "1. I can understand why the ablation study section is too short. I'm wondering if the author can present the scores just using CRF, unifying the two-step architecture into one CRF, and show how much degradation is in the ablation study? This can also answer why the two-step model performs better than other learning-based models.\n2. Among \"errors without obvious reasons\", how many are associated with ellipsis (like the example in Table 8)? Can this error be counted as one major error type?", "limitations": "", "rating": 3, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for taking the time to review our work.\n\nResponse to Reasons to Reject:\n1. We consider coordinating conjunctions that join two or more elements of equal grammatical rank and syntactic importance, which can be noun phrases, verb phrases, clauses, etc.    \nThank you for your suggestion and we will incorporate an example to illustrate the coordination recognition task in the final version of the paper.\n2. Both the “Simple” and “Complex” refer to the complexity of the coordinators, rather than indicating levels of difficulty. “Simple” set contains “and”, “but”, “or” as target coordinators only. These three coordinators can be handled by all baselines. Whereas “Complex” set contains other coordinators, including multi-token coordinators (e.g. “as well as”) and disjoint coordinators (e.g. “not only...but also...”). For a more comprehensive list of “complex” cases, kindly refer to Appendix B. Learning-based coordination recognition baseline methods either do not recognize these cases as coordinators (e.g. “as well as”), or treat only a single token as the coordinator (e.g. only “but” in “not only...but also...” is considered as the coordinator). \n\nResponse to Questions:\n1. Does the “two-step architecture” refer to recognizing coordinators and recognizing conjuncts in two separate steps?   \nThe reason we do not unify these two steps into one CRF is that we also need to handle sentences with nested coordination such as this example “That should [stop a lot of account-churning] and [produce a stock market driven only by {professional concern}, {careful thought} and {good sense}].” from Penn Treebank WSJ (we use [] and {} to highlight the conjunct boundaries). To handle the nested coordination, the target coordinator needs to be specified first. \n2. Out of the 16 sentences categorized as \"errors without obvious reasons\", only 2 sentences are associated with ellipsis. Given this occurrence, these error instances might not be considered as a major error type.    \nThe following is another example from this category:   \nSentence: Just the fact 0 we ' re on 24 hours is no longer bulletin , ' ' says Ed Turner , CNN ' s executive vice president , news gathering - lrb - and no relation to Ted Turner - rrb - .   \nPrediction: Just the fact 0 we ' re on 24 hours is no longer bulletin , ' ' says Ed Turner , CNN ' s executive vice president , news gathering - lrb [-] and [no relation to Ted Turner -] rrb - .   \nWe use [] to highlight the predicted conjunct boundaries.    \nWe suspect the special symbols (- lrb - and - rrb - refer to left and right round brackets, respectively) in this sentence might cause misunderstandings of BERT.    \nOverall, we did not find obvious patterns for those errors. Therefore, we categorized them as \"errors without obvious reasons\".", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper avoids the existing parser's problems with coordination and provides a better solution to recognize coordination structures. The model achieves the sota performance on three benchmarks.\n2. The model exhibits superior efficiency when compared to other models.", "reasons_to_reject": "1. The task definition is not straightforward and clear to audiences. What is the extent of coordination for this task —— does it refer to intra-sentential coordination that connects nominal phrases or verbal phrases, or does it also include inter-sentential discourse relations? Can authors also provide an example on page 1 or 2 to illustrate the coordination recognition task?\n2. The classification of \"simple\" and \"complex\" coordinators (or maybe just their names) is not clear to me. If \"complex\" coordinators may not be hard to predict, I would be confused with its name when reading the paper.\n3. Please refer to questions.", "questions_to_authors": "1. I can understand why the ablation study section is too short. I'm wondering if the author can present the scores just using CRF, unifying the two-step architecture into one CRF, and show how much degradation is in the ablation study? This can also answer why the two-step model performs better than other learning-based models.\n2. Among \"errors without obvious reasons\", how many are associated with ellipsis (like the example in Table 8)? Can this error be counted as one major error type?", "paper_topic_and_main_contributions": "Coordination recognition refers to a task that identifies the coordination structure in sentences, which is beneficial for a variety of NLP tasks, including Open IE. In contrast to previous research that relies on parser outputs, this paper proposes a two-step solution. The first step is to identify coordinators and the second step solves the structure by finding relevant arguments from its neighbors. Experiments show that the CRF model helps find the boundaries of coordination structures, present that the model achieves the sota scores across three benchmark datasets, and demonstrates the efficiency of the model. Additionally, the split sentences that generate from model predictions contribute to the downstream NLP task, Open IE.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "1. L240/Table 1, ontoNotes -> OntoNotes"}, "extra_text": {}}, {"review_id": "cKqMZHjEOj", "reviewer": "Reviewer_r3Vf", "summary": "", "questions": "A. Have you considered conjunction over larger constituents such as verb phrases or clauses (\"J likes games and K does too\") ?\n\nB. Could you elaborate on what goes into the runtimes you report? You assert in your introduction that parsers are slow, which is one of the reasons you took the direction you did. Just what is being measured in the times reported in Table 1. \"Inference time\" is not a transparent concept. What unit of text is it being measured over and what are you counting? OpenIE6, for example, reports speed as sentences per second (its table 2). \nRe. author's response. I was looking a timing definition that was more like OpenIE6's: some quantity of text tokens per unit of time. Saying that it reflects the time to get through a particular data set just pushes the question further back to what is in the data set.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 5, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We really appreciate your reviews and suggestions.   \n1. (A.) We have considered conjunctions over larger constituents such as verb phrases and clauses.    \nA pertinent example can be found in the Genia testing dataset: “Our results suggest [that CaMKIV may represent a physiologically relevant CREB kinase in T cells] and [that the enzyme is also required to ensure the normal expansion of T cells in the thymus].”   \nWe have also considered nested coordination such as this example “That should [stop a lot of account-churning] and [produce a stock market driven only by {professional concern}, {careful thought} and {good sense}].” from Penn Treebank WSJ (we use [] and {} to highlight the conjunct boundaries).   \nThe average lengths of conjuncts in ontoNotes, Genia, and Penn are 4.87, 4.78, and 5.60, respectively.\n2. (B.) The reported runtimes correspond to the number of seconds required for each specific testing dataset to be completed (get the conjuncts detected) by each method.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "-- Makes judicious use of just enough LLM technology to process its training data and uses a clear cascading architecture of straightforward components rather than\n trying a complex custom transformer architecture.\n\n-- Quite clearly written, with a sensible split between the body argument and details in an short appendix (all in 8 pages).\n\n-- Includes an informative error analysis and a persuasive ablation study.", "reasons_to_reject": "I see no reason to reject this paper", "questions_to_authors": "A. Have you considered conjunction over larger constituents such as verb phrases or clauses (\"J likes games and K does too\") ?\n\nB. Could you elaborate on what goes into the runtimes you report? You assert in your introduction that parsers are slow, which is one of the reasons you took the direction you did. Just what is being measured in the times reported in Table 1. \"Inference time\" is not a transparent concept. What unit of text is it being measured over and what are you counting? OpenIE6, for example, reports speed as sentences per second (its table 2). \nRe. author's response. I was looking a timing definition that was more like OpenIE6's: some quantity of text tokens per unit of time. Saying that it reflects the time to get through a particular data set just pushes the question further back to what is in the data set.", "paper_topic_and_main_contributions": "This paper presents a process for the identification and demarcation of conjunctive phrases in open text. Having accurate conjunctions is important for open information extraction to establish scope and clear bounds for what the IE system is targeting. Their system, CoRec, is a cascade of two independently trained processes. The training is based on the Penn Treebank with the usual split. The first process identifies the conjuncts ('coordinators') and introduces markers around their span. The second process employs BIO labels on constituents and position information derived from the training. Their model is built using a fine-tuned BERT encoder. \n\nIn addition to simple conjuncts such as 'and', 'but' or 'as well as'.  Their system handles paired conjunctions: 'either .. or', and the 'respectively' construction. In addition to Treebank, they test their system on OntoNotes and the GENIA biology data set, consistently outperforming their parsing-based baselines and selected earlier learning based methods.", "excitement": 4, "reproducibility": 5, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "Overall this is paper is clearly written. This reviewer's only complaint is the notation is the technical core of the paper (sect. 3.3 through 3.5) should be introduced a bit more slowly, noting the meaning of the symbols rather than requiring us to puzzle them out, e.g. the 'cbd' superscript."}}, {"review_id": "GfmZzyO6kj", "reviewer": "Reviewer_12p3", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your reviews and questions.   \nThe “respectively” case is handled differently in training and inference.    \nDuring inference, when encountering a sentence containing “respectively”, we consider the conjuncts recognized when “respectively” is the target coordinator span as the final result.   \nDuring training, for “and … and … respectively” sentence, we recognize three coordinator spans (“and”, “and”, and “respectively”) and generate three training instances with different target coordinator spans (as shown in Table 7). This is because “and” is a one the most common coordinators and occurs much more frequently than “and … and … respectively”. If we only consider “respectively” as the sole target coordinator in the sentence and do not consider “and” as a coordinator during training, the model can be confused.   \nWe apologize for any confusion caused and we will enhance the clarity of relevant descriptions in the final version of the paper.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The accuracy improvement on the Genia data is significant.", "reasons_to_reject": "Description of the method is not satisfactorily thorough even considering the space limitation.\nIt is not imaginable how to handle the conjuncts of the paired span coordinators and coordination with “respectively\" without referring to the appendix. The case for “respectively\" is not clear to me even after reading the appendix. How the three sequence labeling results in Table 7 are merged (if the predictions are not consistent)?", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper presents a method for coordination structure analysis based on BERT.\nBy formulating the conjunct boundary detection as a sequence labeling problem, unlike previous methods, it handles a coordination including more than two conjuncts straightforwardly and efficiently.\nIt brought modest improvement on OntoNote and PennWSJ data, and significant improvement on Genia Corpus.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "* L214: the the -> the\n* L252: in 4 -> in Section 4"}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper proposes a new approach to conjunction recognition. The method consists of two steps: (1) identification of coordinators and (2) filling in relevant arguments through sequence labeling. The method outperforms regular parsers on three datasets and improves the performance of the downstream task (information extraction).

The reviewers agreed while ranking **soundness** of this paper as strong (scores 4, 4, and 5). Among the strengths of this submission, they listed a *straightforward architecture* and *informative error analysis*. 
In the first round, the reviewers had many questions regarding the details of the proposed method. However, the answers from the authors clarified most of the issues. Therefore, it is strongly recommended that these explanations and clarifications be included in the camera-ready version.

Regarding **excitement**, the reviewers chose more neutral scores (3, 3, and 4). On the one hand, they were convinced that the paper provides a *good solution* to the described problem. The proposed model achieves *state-of-the-art performance* and tackles concrete shortcomings of other approaches. On the other hand, conjunction recognition is a very specialized task. Therefore, the paper needs to be clear and provide all the necessary details to reach a broad audience. The reviewers found that this level of detail needs to be improved.

To sum up, it is a sound submission about a particular task. It requires clarification and details that can be added in the camera-ready version. When the paper is clearer, it will also make its readers more excited.
Decision: Accept-Main