pwc-archive/papers-with-abstracts huggingface dataset:
Samsung R&D Institute Poland submission to WAT 2021 Indic Language Multilingual Task
Authors: Adam Dobrowolski, Marcin Szymański, Marcin Chochowski, Paweł Przybysz
Date: 2021-08-01 00:00:00
Proceeding: ACL (WAT) 2021 8
Tasks: Domain Adaptation, Knowledge Distillation, NMT, Translation, Transliteration

This paper describes the submission to the WAT 2021 Indic Language Multilingual Task by Samsung R&D Institute Poland. The task covered translation between 10 Indic Languages (Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil and Telugu) and English. We combined a variety of techniques: transliteration, filtering, backtranslation, domain adaptation, knowledge-distillation and finally ensembling of NMT models. We applied an effective approach to low-resource training that consist of pretraining on backtranslations and tuning on parallel corpora. We experimented with two different domain-adaptation techniques which significantly improved translation quality when applied to monolingual corpora. We researched and applied a novel approach for finding the best hyperparameters for ensembling a number of translation models. All techniques combined gave significant improvement - up to +8 BLEU over baseline results. The quality of the models has been confirmed by the human evaluation where SRPOL models scored best for all 5 manually evaluated languages.
Multilingual Machine Translation Systems at WAT 2021: One-to-Many and Many-to-One Transformer based NMT
Authors: Shivam Mhaskar, Aditya Jain, Aakash Banerjee, Pushpak Bhattacharyya
Date: 2021-08-01 00:00:00
Proceeding: ACL (WAT) 2021 8
Tasks: Decoder, Machine Translation, NMT

In this paper, we present the details of the systems that we have submitted for the WAT 2021 MultiIndicMT: An Indic Language Multilingual Task. We have submitted two separate multilingual NMT models: one for English to 10 Indic languages and another for 10 Indic languages to English. We discuss the implementation details of two separate multilingual NMT approaches, namely one-to-many and many-to-one, that makes use of a shared decoder and a shared encoder, respectively. From our experiments, we observe that the multilingual NMT systems outperforms the bilingual baseline MT systems for each of the language pairs under consideration.
IITP-MT at WAT2021: Indic-English Multilingual Neural Machine Translation using Romanized Vocabulary
Authors: Ramakrishna Appicharla, Kamal Kumar Gupta, Asif Ekbal, Pushpak Bhattacharyya
Date: 2021-08-01 00:00:00
Proceeding: ACL (WAT) 2021 8
Tasks: Machine Translation, NMT, Translation

This paper describes the systems submitted to WAT 2021 MultiIndicMT shared task by IITP-MT team. We submit two multilingual Neural Machine Translation (NMT) systems (Indic-to-English and English-to-Indic). We romanize all Indic data and create subword vocabulary which is shared between all Indic languages. We use back-translation approach to generate synthetic data which is appended to parallel corpus and used to train our models. The models are evaluated using BLEU, RIBES and AMFM scores with Indic-to-English model achieving 40.08 BLEU for Hindi-English pair and English-to-Indic model achieving 34.48 BLEU for English-Hindi pair. However, we observe that the shared romanized subword vocabulary is not helping English-to-Indic model at the time of generation, leading it to produce poor quality translations for Tamil, Telugu and Malayalam to English pairs with BLEU score of 8.51, 6.25 and 3.79 respectively.
ANVITA Machine Translation System for WAT 2021 MultiIndicMT Shared Task
Authors: Pavanpankaj Vegi, Sivabhavani J, Biswajit Paul, Chitra Viswanathan, Prasanna Kumar K R
Date: 2021-08-01 00:00:00
Proceeding: ACL (WAT) 2021 8
Tasks: Data Augmentation, Decoder, Machine Translation, NMT, Translation, Transliteration

This paper describes ANVITA-1.0 MT system, architected for submission to WAT2021 MultiIndicMT shared task by mcairt team, where the team participated in 20 translation directions: English→Indic and Indic→English; Indic set comprised of 10 Indian languages. ANVITA-1.0 MT system comprised of two multi-lingual NMT models one for the English→Indic directions and other for the Indic→English directions with shared encoder-decoder, catering 10 language pairs and twenty translation directions. The base models were built based on Transformer architecture and trained over MultiIndicMT WAT 2021 corpora and further employed back translation and transliteration for selective data augmentation, and model ensemble for better generalization. Additionally, MultiIndicMT WAT 2021 corpora was distilled using a series of filtering operations before putting up for training. ANVITA-1.0 achieved highest AM-FM score for English→Bengali, 2nd for English→Tamil and 3rd for English→Hindi, Bengali→English directions on official test set. In general, performance achieved by ANVITA for the Indic→English directions are relatively better than that of English→Indic directions for all the 10 language pairs when evaluated using BLEU and RIBES, although the same trend is not observed consistently when AM-FM based evaluation was carried out. As compared to BLEU, RIBES and AM-FM based scoring placed ANVITA relatively better among all the task participants.
Task Definition and Integration For Scientific-Document Writing Support
Authors: Hiromi Narimatsu, Kohei Koyama, Kohji Dohsaka, Ryuichiro Higashinaka, Yasuhiro Minami, Hirotoshi Taira
Date: 2021-06-01 00:00:00
Proceeding: NAACL (sdp) 2021 6
Tasks: Citation Recommendation, Citation worthiness, Sentence, Text Generation

With the increase in the number of published academic papers, growing expectations have been placed on research related to supporting the writing process of scientific papers. Recently, research has been conducted on various tasks such as citation worthiness (judging whether a sentence requires citation), citation recommendation, and citation-text generation. However, since each task has been studied and evaluated using data that has been independently developed, it is currently impossible to verify whether such tasks can be successfully pipelined to effective use in scientific-document writing. In this paper, we first define a series of tasks related to scientific-document writing that can be pipelined. Then, we create a dataset of academic papers that can be used for the evaluation of each task as well as a series of these tasks. Finally, using the dataset, we evaluate the tasks of citation worthiness and citation recommendation as well as both of these tasks integrated. The results of our evaluations show that the proposed approach is promising.
Detecting Anatomical and Functional Connectivity Relations in Biomedical Literature via Language Representation Models
Authors: Ibrahim Burak Ozyurt, Joseph Menke, Anita Bandrowski, Maryann Martone
Date: 2021-06-01 00:00:00
Proceeding: NAACL (sdp) 2021 6
Tasks: Active Learning, Functional Connectivity, Relation, Relation Extraction

Understanding of nerve-organ interactions is crucial to facilitate the development of effective bioelectronic treatments. Towards the end of developing a systematized and computable wiring diagram of the autonomic nervous system (ANS), we introduce a curated ANS connectivity corpus together with several neural language representation model based connectivity relation extraction systems. We also show that active learning guided curation for labeled corpus expansion significantly outperforms randomly selecting connectivity relation candidates minimizing curation effort. Our final relation extraction system achieves F_1 = 72.8% on anatomical connectivity and F_1 = 74.6% on functional connectivity relation extraction.
The Biomaterials Annotator: a system for ontology-based concept annotation of biomaterials text
Authors: Javier Corvi, Carla Fuenteslópez, José Fernández, Josep Gelpi, Maria-Pau Ginebra, Salvador Capella-Guitierrez, Osnat Hakimi
Date: 2021-06-01 00:00:00
Proceeding: NAACL (sdp) 2021 6
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Biomaterials are synthetic or natural materials used for constructing artificial organs, fabricating prostheses, or replacing tissues. The last century saw the development of thousands of novel biomaterials and, as a result, an exponential increase in scientific publications in the field. Large-scale analysis of biomaterials and their performance could enable data-driven material selection and implant design. However, such analysis requires identification and organization of concepts, such as materials and structures, from published texts. To facilitate future information extraction and the application of machine-learning techniques, we developed a semantic annotator specifically tailored for the biomaterials literature. The Biomaterials Annotator has been implemented following a modular organization using software containers for the different components and orchestrated using Nextflow as workflow manager. Natural language processing (NLP) components are mainly developed in Java. This set-up has allowed named entity recognition of seventeen classes relevant to the biomaterials domain. Here we detail the development, evaluation and performance of the system, as well as the release of the first collection of annotated biomaterials abstracts. We make both the corpus and system available to the community to promote future efforts in the field and contribute towards its sustainability.
Keyphrase Extraction from Scientific Articles via Extractive Summarization
Authors: Chrysovalantis Giorgos Kontoulis, Eirini Papagiannopoulou, Grigorios Tsoumakas
Date: 2021-06-01 00:00:00
Proceeding: NAACL (sdp) 2021 6
Tasks: Articles, Extractive Summarization, Information Retrieval, Keyphrase Extraction, Retrieval

Automatically extracting keyphrases from scholarly documents leads to a valuable concise representation that humans can understand and machines can process for tasks, such as information retrieval, article clustering and article classification. This paper is concerned with the parts of a scientific article that should be given as input to keyphrase extraction methods. Recent deep learning methods take titles and abstracts as input due to the increased computational complexity in processing long sequences, whereas traditional approaches can also work with full-texts. Titles and abstracts are dense in keyphrases, but often miss important aspects of the articles, while full-texts on the other hand are richer in keyphrases but much noisier. To address this trade-off, we propose the use of extractive summarization models on the full-texts of scholarly documents. Our empirical study on 3 article collections using 3 keyphrase extraction methods shows promising results.
Argument Mining for Scholarly Document Processing: Taking Stock and Looking Ahead
Authors: Khalid Al Khatib, Tirthankar Ghosal, Yufang Hou, Anita de Waard, Dayne Freitag
Date: 2021-06-01 00:00:00
Proceeding: NAACL (sdp) 2021 6
Tasks: Argument Mining, Articles

Argument mining targets structures in natural language related to interpretation and persuasion which are central to scientific communication. Most scholarly discourse involves interpreting experimental evidence and attempting to persuade other scientists to adopt the same conclusions. While various argument mining studies have addressed student essays and news articles, those that target scientific discourse are still scarce. This paper surveys existing work in argument mining of scholarly discourse, and provides an overview of current models, data, tasks, and applications. We identify a number of key challenges confronting argument mining in the scientific domain, and suggest some possible solutions and future directions.
Bootstrapping Multilingual Metadata Extraction: A Showcase in Cyrillic
Authors: Johan Krause, Igor Shapiro, Tarek Saier, Michael Färber
Date: 2021-06-01 00:00:00
Proceeding: NAACL (sdp) 2021 6

Applications based on scholarly data are of ever increasing importance. This results in disadvantages for areas where high-quality data and compatible systems are not available, such as non-English publications. To advance the mitigation of this imbalance, we use Cyrillic script publications from the CORE collection to create a high-quality data set for metadata extraction. We utilize our data for training and evaluating sequence labeling models to extract title and author information. Retraining GROBID on our data, we observe significant improvements in terms of precision and recall and achieve even better results with a self developed model. We make our data set covering over 15,000 publications as well as our source code freely available.
The Effect of Pretraining on Extractive Summarization for Scientific Documents
Authors: Yash Gupta, Pawan Sasanka Ammanamanchi, Shikha Bordia, Arjun Manoharan, Deepak Mittal, Ramakanth Pasunuru, Manish Shrivastava, Maneesh Singh, Mohit Bansal, Preethi Jyothi
Date: 2021-06-01 00:00:00
Proceeding: NAACL (sdp) 2021 6
Tasks: Extractive Summarization, Word Embeddings

Large pretrained models have seen enormous success in extractive summarization tasks. In this work, we investigate the influence of pretraining on a BERT-based extractive summarization system for scientific documents. We derive significant performance improvements using an intermediate pretraining step that leverages existing summarization datasets and report state-of-the-art results on a recently released scientific summarization dataset, SciTLDR. We systematically analyze the intermediate pretraining step by varying the size and domain of the pretraining corpus, changing the length of the input sequence in the target task and varying target tasks. We also investigate how intermediate pretraining interacts with contextualized word embeddings trained on different domains.
LongSumm 2021: Session based automatic summarization model for scientific document
Authors: Senci Ying, Zheng Yan Zhao, Wuhe Zou
Date: 2021-06-01 00:00:00
Proceeding: NAACL (sdp) 2021 6
Tasks: Language Modeling, Language Modelling

Most summarization task focuses on generating relatively short summaries. Such a length constraint might not be appropriate when summarizing scientific work. The LongSumm task needs participants generate long summary for scientific document. This task usual can be solved by language model. But an important problem is that model like BERT is limit to memory, and can not deal with a long input like a document. Also generate a long output is hard. In this paper, we propose a session based automatic summarization model(SBAS) which using a session and ensemble mechanism to generate long summary. And our model achieves the best performance in the LongSumm task.
CNLP-NITS @ LongSumm 2021: TextRank Variant for Generating Long Summaries
Authors: Darsh Kaushik, Abdullah Faiz Ur Rahman Khilji, Utkarsh Sinha, Partha Pakray
Date: 2021-06-01 00:00:00
Proceeding: NAACL (sdp) 2021 6
Tasks: Extractive Summarization

The huge influx of published papers in the field of machine learning makes the task of summarization of scholarly documents vital, not just to eliminate the redundancy but also to provide a complete and satisfying crux of the content. We participated in LongSumm 2021: The 2^{nd} Shared Task on Generating Long Summaries for scientific documents, where the task is to generate long summaries for scientific papers provided by the organizers. This paper discusses our extractive summarization approach to solve the task. We used TextRank algorithm with the BM25 score as a similarity function. Even after being a graph-based ranking algorithm that does not require any learning, TextRank produced pretty decent results with minimal compute power and time. We attained 3^{rd} rank according to ROUGE-1 scores (0.5131 for F-measure and 0.5271 for recall) and performed decently as shown by the ROUGE-2 scores.
Unsupervised document summarization using pre-trained sentence embeddings and graph centrality
Authors: Juan Ramirez-Orta, Evangelos Milios
Date: 2021-06-01 00:00:00
Proceeding: NAACL (sdp) 2021 6
Tasks: Abstractive Text Summarization, Document Summarization, Extractive Summarization, Sentence, Sentence Embeddings

This paper describes our submission for the LongSumm task in SDP 2021. We propose a method for incorporating sentence embeddings produced by deep language models into extractive summarization techniques based on graph centrality in an unsupervised manner.The proposed method is simple, fast, can summarize any kind of document of any size and can satisfy any length constraints for the summaries produced. The method offers competitive performance to more sophisticated supervised methods and can serve as a proxy for abstractive summarization techniques
SciBERT Sentence Representation for Citation Context Classification
Authors: Himanshu Maheshwari, Bhavyajeet Singh, Vasudeva Varma
Date: 2021-06-01 00:00:00
Proceeding: NAACL (sdp) 2021 6
Tasks: Classification, Sentence

This paper describes our system (IREL) for 3C-Citation Context Classification shared task of the Scholarly Document Processing Workshop at NAACL 2021. We participated in both subtask A and subtask B. Our best system achieved a Macro F1 score of 0.26973 on the private leaderboard for subtask A and was ranked one. For subtask B our best system achieved a Macro F1 score of 0.59071 on the private leaderboard and was ranked two. We used similar models for both the subtasks with some minor changes, as discussed in this paper. Our best performing model for both the subtask was a finetuned SciBert model followed by a linear layer. This paper provides a detailed description of all the approaches we tried and their results.
IITP-CUNI@3C: Supervised Approaches for Citation Classification (Task A) and Citation Significance Detection (Task B)
Authors: Kamal Kaushik Varanasi, Tirthankar Ghosal, Piyush Tiwary, Muskaan Singh
Date: 2021-06-01 00:00:00
Proceeding: NAACL (sdp) 2021 6
Tasks: Classification, Multi-Task Learning

Citations are crucial to a scientific discourse. Besides providing additional contexts to research papers, citations act as trackers of the direction of research in a field and as an important measure in understanding the impact of a research publication. With the rapid growth in research publications, automated solutions for identifying the purpose and influence of citations are becoming very important. The 3C Citation Context Classification Task organized as part of the Second Workshop on Scholarly Document Processing @ NAACL 2021 is a shared task to address the aforementioned problems. In this paper, we present our team, IITP-CUNI@3C’s submission to the 3C shared tasks. For Task A, citation context purpose classification, we propose a neural multi-task learning framework that harnesses the structural information of the research papers and the relation between the citation context and the cited paper for citation classification. For Task B, citation context influence classification, we use a set of simple features to classify citations based on their perceived significance. We achieve comparable performance with respect to the best performing systems in Task A and superseded the majority baseline in Task B with very simple features.
Amrita_CEN_NLP@SDP2021 Task A and B
Authors: Premjith B, Isha Indhu S, Kavya S. Kumar, Lakshaya Karthikeyan, Soman Kp
Date: 2021-06-01 00:00:00
Proceeding: NAACL (sdp) 2021 6

The purpose and influence of a citation are important in understanding the quality of a publication. The 3c citation context classification shared task at the Second Workshop on Scholarly Document Processing aims at addressing this problem. This paper is the submission of the team Amrita_CEN_NLP to the shared task. We employed Bi-directional Long Short Term Memory (LSTM) networks and a Random Forest classifier for modelling the aforementioned problems by considering the class imbalance problem in the data.
Overview of the 2021 SDP 3C Citation Context Classification Shared Task
Authors: Suchetha N. Kunnath, David Pride, Drahomira Herrmannova, Petr Knoth
Date: 2021-06-01 00:00:00
Proceeding: NAACL (sdp) 2021 6

This paper provides an overview of the 2021 3C Citation Context Classification shared task. The second edition of the shared task was organised as part of the 2nd Workshop on Scholarly Document Processing (SDP 2021). The task is composed of two subtasks: classifying citations based on their (Subtask A) purpose and (Subtask B) influence. As in the previous year, both tasks were hosted on Kaggle and used a portion of the new ACT dataset. A total of 22 teams participated in Subtask A, and 19 teams competed in Subtask B. All the participated systems were ranked based on their achieved macro f-score. The highest scores of 0.26973 and 0.60025 were reported for subtask A and B, respectively.
Overview of the Second Workshop on Scholarly Document Processing
Authors: Iz Beltagy, Arman Cohan, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Keith Hall, Drahomira Herrmannova, Petr Knoth, Kyle Lo, Philipp Mayr, Robert Patton, Michal Shmueli-Scheuer, Anita de Waard, Kuansan Wang, Lucy Wang
Date: 2021-06-01 00:00:00
Proceeding: NAACL (sdp) 2021 6
Tasks: Information Retrieval, Retrieval

With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task. Not only must they keep up with the growing literature in their own and related fields, scholars increasingly also need to rebut pseudo-science and disinformation. These needs have motivated an increasing focus on computational methods for enhancing search, summarization, and analysis of scholarly documents. However, the various strands of research on scholarly document processing remain fragmented. To reach out to the broader NLP and AI/ML community, pool distributed efforts in this area, and enable shared access to published research, we held the 2nd Workshop on Scholarly Document Processing (SDP) at NAACL 2021 as a virtual event (https://sdproc.org/2021/). The SDP workshop consisted of a research track, three invited talks, and three Shared Tasks (LongSumm 2021, SCIVER, and 3C). The program was geared towards the application of NLP, information retrieval, and data mining for scholarly documents, with an emphasis on identifying and providing solutions to open challenges.
A Long Hard Look at MWEs in the Age of Language Models
Authors: Vered Shwartz
Date: 2021-08-01 00:00:00
Proceeding: ACL (MWE) 2021 8
Tasks: World Knowledge

In recent years, language models (LMs) have become almost synonymous with NLP. Pre-trained to “read” a large text corpus, such models are useful as both a representation layer as well as a source of world knowledge. But how well do they represent MWEs? This talk will discuss various problems in representing MWEs, and the extent to which LMs address them: • Do LMs capture the implicit relationship between constituents in compositional MWEs (from baby oil through parsley cake to cheeseburger stabbing)? • Do LMs recognize when words are used nonliterally in non-compositional MWEs (e.g. do they know whether there are fleas in the flea market)? • Do LMs know idioms, and can they infer the meaning of new idioms from the context as humans often do?
Where Do Aspectual Variants of Light Verb Constructions Belong?
Authors: Aggeliki Fotopoulou, Eric Laporte, Takuya Nakamura
Date: 2021-08-01 00:00:00
Proceeding: ACL (MWE) 2021 8

Expressions with an aspectual variant of a light verb, e.g. ‘take on debt’ vs. ‘have debt’, are frequent in texts but often difficult to classify between verbal idioms, light verb constructions or compositional phrases. We investigate the properties of such expressions with a disputed membership and propose a selection of features that determine more satisfactory boundaries between the three categories in this zone, assigning the expressions to one of them.