pwc-archive/papers-with-abstracts huggingface dataset:
NUIG: Multitasking Self-attention based approach to SigTyp 2020 Shared Task
Authors: Chinmay Choudhary
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (SIGTYP) 2020 11

The paper describes the Multitasking Self-attention based approach to constrained sub-task within Sigtyp 2020 Shared task. Our model is simple neural network based architecture inspired by Transformers (CITATION) model. The model uses Multitasking to compute values of all WALS features for a given input language simultaneously.

Results show that our approach performs at par with the baseline approaches, even though our proposed approach requires only phylogenetic and geographical attributes namely Longitude, Latitude, Genus-index, Family-index and Country-index and do not use any of the known WALS features of the respective input language, to compute its missing WALS features.
FanfictionNLP: A Text Processing Pipeline for Fanfiction
Authors: Michael Yoder, Sopan Khosla, Qinlan Shen, Aakanksha Naik, Huiming Jin, Hariharan Muralidharan, Carolyn Rosé
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NUSE) 2021 6

Fanfiction presents an opportunity as a data source for research in NLP, education, and social science. However, answering specific research questions with this data is difficult, since fanfiction contains more diverse writing styles than formal fiction. We present a text processing pipeline for fanfiction, with a focus on identifying text associated with characters. The pipeline includes modules for character identification and coreference, as well as the attribution of quotes and narration to those characters. Additionally, the pipeline contains a novel approach to character coreference that uses knowledge from quote attribution to resolve pronouns within quotes. For each module, we evaluate the effectiveness of various approaches on 10 annotated fanfiction stories. This pipeline outperforms tools developed for formal fiction on the tasks of character coreference and quote attribution
Gender and Representation Bias in GPT-3 Generated Stories
Authors: Li Lucy, David Bamman
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NUSE) 2021 6
Tasks: Word Similarity

Using topic modeling and lexicon-based word similarity, we find that stories generated by GPT-3 exhibit many known gender stereotypes. Generated stories depict different topics and descriptions depending on GPT-3’s perceived gender of the character in a prompt, with feminine characters more likely to be associated with family and appearance, and described as less powerful than masculine characters, even when associated with high power verbs in a prompt. Our study raises questions on how one can avoid unintended social biases when using large language models for storytelling.
Transformer-based Screenplay Summarization Using Augmented Learning Representation with Dialogue Information
Authors: Myungji Lee, HongSeok Kwon, Jaehun Shin, WonKee Lee, Baikjin Jung, Jong-Hyeok Lee
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NUSE) 2021 6
Tasks: Attribute

Screenplay summarization is the task of extracting informative scenes from a screenplay. The screenplay contains turning point (TP) events that change the story direction and thus define the story structure decisively. Accordingly, this task can be defined as the TP identification task. We suggest using dialogue information, one attribute of screenplays, motivated by previous work that discovered that TPs have a relation with dialogues appearing in screenplays. To teach a model this characteristic, we add a dialogue feature to the input embedding. Moreover, in an attempt to improve the model architecture of previous studies, we replace LSTM with Transformer. We observed that the model can better identify TPs in a screenplay by using dialogue information and that a model adopting Transformer outperforms LSTM-based models.
Towards a Model-Theoretic View of Narratives
Authors: Louis Castricato, Stella Biderman, David Thue, Rogelio Cardona-Rivera
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NUSE) 2021 6
Tasks: model

In this paper, we propose the beginnings of a formal framework for modeling narrative qua narrative. Our framework affords the ability to discuss key qualities of stories and their communication, including the flow of information from a Narrator to a Reader, the evolution of a Reader’s story model over time, and Reader uncertainty. We demonstrate its applicability to computational narratology by giving explicit algorithms for measuring the accuracy with which information was conveyed to the Reader, along with two novel measurements of story coherence.
A Brief Survey and Comparative Study of Recent Development of Pronoun Coreference Resolution in English
Authors: Hongming Zhang, Xinran Zhao, Yangqiu Song
Date: 2021-11-01 00:00:00
Proceeding: CRAC (ACL) 2021 11
Tasks: coreference-resolution, Coreference Resolution, Natural Language Understanding, Relation Prediction, Survey

Pronoun Coreference Resolution (PCR) is the task of resolving pronominal expressions to all mentions they refer to. Compared with the general coreference resolution task, the main challenge of PCR is the coreference relation prediction rather than the mention detection. As one important natural language understanding (NLU) component, pronoun resolution is crucial for many downstream tasks and still challenging for existing models, which motivates us to survey existing approaches and think about how to do better. In this survey, we first introduce representative datasets and models for the ordinary pronoun coreference resolution task. Then we focus on recent progress on hard pronoun coreference resolution problems (e.g., Winograd Schema Challenge) to analyze how well current models can understand commonsense. We conduct extensive experiments to show that even though current models are achieving good performance on the standard evaluation set, they are still not ready to be used in real applications (e.g., all SOTA models struggle on correctly resolving pronouns to infrequent objects). All experiment codes will be available upon acceptance.
FantasyCoref: Coreference Resolution on Fantasy Literature Through Omniscient Writer’s Point of View
Authors: Sooyoun Han, Sumin Seo, Minji Kang, Jongin Kim, Nayoung Choi, Min Song, Jinho D. Choi
Date: 2021-11-01 00:00:00
Proceeding: CRAC (ACL) 2021 11
Tasks: coreference-resolution, Coreference Resolution

This paper presents a new corpus and annotation guideline for a novel coreference resolution task on fictional texts, and analyzes its unique characteristics. FantasyCoref contains 211 stories of Grimms’ Fairy Tales and 3 other fantasy literature annotated in the omniscient writer’s point of view (OWV) to handle distinctive aspects in this genre. This task is more challenging than general coreference resolution in two ways. First, documents in our corpus are 2.5 times longer than the ones in OntoNotes, raising a new layer of difficulty in resolving long-distant referents. Second, annotation of literary styles and concepts raise several issues which are not sufficiently addressed in the existing annotation guidelines. Hence, considerations on such issues and the concept of OWV are necessary to achieve high inter-annotator agreement (IAA) in coreference resolution of fictional texts. We carefully conduct annotation tasks in four stages to ensure the quality of our annotation. As a result, a high IAA score of 87% is achieved using the standard coreference evaluation metric. Finally, state-of-the-art coreference resolution approaches are evaluated on our corpus. After training with our annotated dataset, there was a 2.59% and 3.06% improvement over the model trained on the OntoNotes dataset. Also, we observe that the portion of errors specific to fictional texts declines after the training.
DramaCoref: A Hybrid Coreference Resolution System for German Theater Plays
Authors: Janis Pagel, Nils Reiter
Date: 2021-11-01 00:00:00
Proceeding: CRAC (ACL) 2021 11
Tasks: coreference-resolution, Coreference Resolution

We present a system for resolving coreference on theater plays, DramaCoref. The system uses neural network techniques to provide a list of potential mentions. These mentions are assigned to common entities using generic and domain-specific rules. We find that DramaCoref works well on the theater plays when compared to corpora from other domains and profits from the inclusion of information specific to theater plays. On the best-performing setup, it achieves a CoNLL score of 32% when using automatically detected mentions and 55% when using gold mentions. Single rules achieve high precision scores; however, rules designed on other domains are often not applicable or yield unsatisfactory results. Error analysis shows that the mention detection is the main weakness of the system, providing directions for future improvements.
A Hybrid Rule-Based and Neural Coreference Resolution System with an Evaluation on Dutch Literature
Authors: Andreas van Cranenburgh, Esther Ploeger, Frank van den Berg, Remi Thüss
Date: 2021-11-01 00:00:00
Proceeding: CRAC (ACL) 2021 11
Tasks: coreference-resolution, Coreference Resolution, Feature Engineering

We introduce a modular, hybrid coreference resolution system that extends a rule-based baseline with three neural classifiers for the subtasks mention detection, mention attributes (gender, animacy, number), and pronoun resolution. The classifiers substantially increase coreference performance in our experiments with Dutch literature across all metrics on the development set: mention detection, LEA, CoNLL, and especially pronoun accuracy. However, on the test set, the best results are obtained with rule-based pronoun resolution. This inconsistent result highlights that the rule-based system is still a strong baseline, and more work is needed to improve pronoun resolution robustly for this dataset. While end-to-end neural systems require no feature engineering and achieve excellent performance in standard benchmarks with large training sets, our simple hybrid system scales well to long document coreference (>10k words) and attains superior results in our experiments on literature.
Lazy Low-Resource Coreference Resolution: a Study on Leveraging Black-Box Translation Tools
Authors: Semere Kiros Bitew, Johannes Deleu, Chris Develder, Thomas Demeester
Date: 2021-11-01 00:00:00
Proceeding: CRAC (ACL) 2021 11
Tasks: coreference-resolution, Coreference Resolution, Machine Translation, Translation

Large annotated corpora for coreference resolution are available for few languages. For machine translation, however, strong black-box systems exist for many languages. We empirically explore the appealing idea of leveraging such translation tools for bootstrapping coreference resolution in languages with limited resources. Two scenarios are analyzed, in which a large coreference corpus in a high-resource language is used for coreference predictions in a smaller language, i.e., by machine translating either the training corpus or the test data. In our empirical evaluation of coreference resolution using the two scenarios on several medium-resource languages, we find no improvement over monolingual baseline models. Our analysis of the various sources of error inherent to the studied scenarios, reveals that in fact the quality of contemporary machine translation tools is the main limiting factor.
Resources and Evaluations for Danish Entity Resolution
Authors: Maria Barrett, Hieu Lam, Martin Wu, Ophélie Lacroix, Barbara Plank, Anders Søgaard
Date: 2021-11-01 00:00:00
Proceeding: CRAC (ACL) 2021 11
Tasks: coreference-resolution, Coreference Resolution, Entity Disambiguation, Entity Linking, Entity Resolution

Automatic coreference resolution is understudied in Danish even though most of the Danish Dependency Treebank (Buch-Kromann, 2003) is annotated with coreference relations. This paper describes a conversion of its partial, yet well-documented, coreference relations into coreference clusters and the training and evaluation of coreference models on this data. To the best of our knowledge, these are the first publicly available, neural coreference models for Danish. We also present a new entity linking annotation on the dataset using WikiData identifiers, a named entity disambiguation (NED) dataset, and a larger automatically created NED dataset enabling wikily supervised NED models. The entity linking annotation is benchmarked using a state-of-the-art neural entity disambiguation model.
Exploring Pre-Trained Transformers and Bilingual Transfer Learning for Arabic Coreference Resolution
Authors: Bonan Min
Date: 2021-11-01 00:00:00
Proceeding: CRAC (ACL) 2021 11
Tasks: coreference-resolution, Coreference Resolution, Transfer Learning

In this paper, we develop bilingual transfer learning approaches to improve Arabic coreference resolution by leveraging additional English annotation via bilingual or multilingual pre-trained transformers. We show that bilingual transfer learning improves the strong transformer-based neural coreference models by 2-4 F1. We also systemically investigate the effectiveness of several pre-trained transformer models that differ in training corpora, languages covered, and model capacity. Our best model achieves a new state-of-the-art performance of 64.55 F1 on the Arabic OntoNotes dataset. Our code is publicly available at https://github.com/bnmin/arabic_coref.
Event and Entity Coreference using Trees to Encode Uncertainty in Joint Decisions
Authors: Nishant Yadav, Nicholas Monath, Rico Angell, Andrew McCallum
Date: 2021-11-01 00:00:00
Proceeding: CRAC (ACL) 2021 11
Tasks: Clustering

Coreference decisions among event mentions and among co-occurring entity mentions are highly interdependent, thus motivating joint inference. Capturing the uncertainty over each variable can be crucial for inference among multiple dependent variables. Previous work on joint coreference employs heuristic approaches, lacking well-defined objectives, and lacking modeling of uncertainty on each side of the joint problem. We present a new approach of joint coreference, including (1) a formal cost function inspired by Dasgupta’s cost for hierarchical clustering, and (2) a representation for uncertainty of clustering of event and entity mentions, again based on a hierarchical structure. We describe an alternating optimization method for inference that when clustering event mentions, considers the uncertainty of the clustering of entity mentions and vice-versa. We show that our proposed joint model provides empirical advantages over state-of-the-art independent and joint models.
Coreference by Appearance: Visually Grounded Event Coreference Resolution
Authors: Liming Wang, Shengyu Feng, Xudong Lin, Manling Li, Heng Ji, Shih-Fu Chang
Date: 2021-11-01 00:00:00
Proceeding: CRAC (ACL) 2021 11
Tasks: coreference-resolution, Coreference Resolution, Event Coreference Resolution, Machine Translation, Translation

Event coreference resolution is critical to understand events in the growing number of online news with multiple modalities including text, video, speech, etc. However, the events and entities depicting in different modalities may not be perfectly aligned and can be difficult to annotate, which makes the task especially challenging with little supervision available. To address the above issues, we propose a supervised model based on attention mechanism and an unsupervised model based on statistical machine translation, capable of learning the relative importance of modalities for event coreference resolution. Experiments on a video multimedia event dataset show that our multimodal models outperform text-only systems in event coreference resolution tasks. A careful analysis reveals that the performance gain of the multimodal model especially under unsupervised settings comes from better learning of visually salient events.
Anatomy of OntoGUM—Adapting GUM to the OntoNotes Scheme to Evaluate Robustness of SOTA Coreference Algorithms
Authors: YIlun Zhu, Sameer Pradhan, Amir Zeldes
Date: 2021-11-01 00:00:00
Proceeding: CRAC (ACL) 2021 11
Tasks: Anatomy, coreference-resolution, Coreference Resolution

SOTA coreference resolution produces increasingly impressive scores on the OntoNotes benchmark. However lack of comparable data following the same scheme for more genres makes it difficult to evaluate generalizability to open domain data. Zhu et al. (2021) introduced the creation of the OntoGUM corpus for evaluating geralizability of the latest neural LM-based end-to-end systems. This paper covers details of the mapping process which is a set of deterministic rules applied to the rich syntactic and discourse annotations manually annotated in the GUM corpus. Out-of-domain evaluation across 12 genres shows nearly 15-20% degradation for both deterministic and deep learning systems, indicating a lack of generalizability or covert overfitting in existing coreference resolution models.
Finding the needle in a haystack: Extraction of Informative COVID-19 Danish Tweets
Authors: Benjamin Olsen, Barbara Plank
Date: 2021-11-01 00:00:00
Proceeding: WNUT (ACL) 2021 11

Finding informative COVID-19 posts in a stream of tweets is very useful to monitor health-related updates. Prior work focused on a balanced data setup and on English, but informative tweets are rare, and English is only one of the many languages spoken in the world. In this work, we introduce a new dataset of 5,000 tweets for finding informative COVID-19 tweets for Danish. In contrast to prior work, which balances the label distribution, we model the problem by keeping its natural distribution. We examine how well a simple probabilistic model and a convolutional neural network (CNN) perform on this task. We find a weighted CNN to work well but it is sensitive to embedding and hyperparameter choices. We hope the contributed dataset is a starting point for further work in this direction.
Keyphrase Extraction with Incomplete Annotated Training Data
Authors: Yanfei Lei, Chunming Hu, Guanghui Ma, Richong Zhang
Date: 2021-11-01 00:00:00
Proceeding: WNUT (ACL) 2021 11
Tasks: Keyphrase Extraction

Extracting keyphrases that summarize the main points of a document is a fundamental task in natural language processing. Supervised approaches to keyphrase extraction(KPE) are largely developed based on the assumption that the training data is fully annotated. However, due to the difficulty of keyphrase annotating, KPE models severely suffer from incomplete annotated problem in many scenarios. To this end, we propose a more robust training method that learns to mitigate the misguidance brought by unlabeled keyphrases. We introduce negative sampling to adjust training loss, and conduct experiments under different scenarios. Empirical studies on synthetic datasets and open domain dataset show that our model is robust to incomplete annotated problem and surpasses prior baselines. Extensive experiments on five scientific domain datasets of different scales demonstrate that our model is competitive with the state-of-the-art method.
Fine-grained Temporal Relation Extraction with Ordered-Neuron LSTM and Graph Convolutional Networks
Authors: Minh Tran Phu, Minh Van Nguyen, Thien Huu Nguyen
Date: 2021-11-01 00:00:00
Proceeding: WNUT (ACL) 2021 11
Tasks: Deep Learning, Relation, Relation Extraction, Representation Learning, Temporal Relation Extraction, Transfer Learning

Fine-grained temporal relation extraction (FineTempRel) aims to recognize the durations and timeline of event mentions in text. A missing part in the current deep learning models for FineTempRel is their failure to exploit the syntactic structures of the input sentences to enrich the representation vectors. In this work, we propose to fill this gap by introducing novel methods to integrate the syntactic structures into the deep learning models for FineTempRel. The proposed model focuses on two types of syntactic information from the dependency trees, i.e., the syntax-based importance scores for representation learning of the words and the syntactic connections to identify important context words for the event mentions. We also present two novel techniques to facilitate the knowledge transfer between the subtasks of FineTempRel, leading to a novel model with the state-of-the-art performance for this task.
Does It Happen? Multi-hop Path Structures for Event Factuality Prediction with Graph Transformer Networks
Authors: Duong Le, Thien Huu Nguyen
Date: 2021-11-01 00:00:00
Proceeding: WNUT (ACL) 2021 11
Tasks: Representation Learning, Sentence

The goal of Event Factuality Prediction (EFP) is to determine the factual degree of an event mention, representing how likely the event mention has happened in text. Current deep learning models has demonstrated the importance of syntactic and semantic structures of the sentences to identify important context words for EFP. However, the major problem with these EFP models is that they only encode the one-hop paths between the words (i.e., the direct connections) to form the sentence structures. In this work, we show that the multi-hop paths between the words are also necessary to compute the sentence structures for EFP. To this end, we introduce a novel deep learning model for EFP that explicitly considers multi-hop paths with both syntax-based and semantic-based edges between the words to obtain sentence structures for representation learning in EFP. We demonstrate the effectiveness of the proposed model via the extensive experiments in this work.
Google-trickers, Yaminjeongeum, and Leetspeak: An Empirical Taxonomy for Intentionally Noisy User-Generated Text
Authors: Won Ik Cho, SooMin Kim
Date: 2021-11-01 00:00:00
Proceeding: WNUT (ACL) 2021 11

WARNING: This article contains contents that may offend the readers. Strategies that insert intentional noise into text when posting it are commonly observed in the online space, and sometimes they aim to let only certain community users understand the genuine semantics. In this paper, we explore the purpose of such actions by categorizing them into tricks, memes, fillers, and codes, and organize the linguistic strategies that are used for each purpose. Through this, we identify that such strategies can be conducted by authors for multiple purposes, regarding the presence of stakeholders such as ‘Peers’ and ‘Others’. We finally analyze how these strategies appear differently in each circumstance, along with the unified taxonomy accompanying examples.
A Text Editing Approach to Joint Japanese Word Segmentation, POS Tagging, and Lexical Normalization
Authors: Shohei Higashiyama, Masao Utiyama, Taro Watanabe, Eiichiro Sumita
Date: 2021-11-01 00:00:00
Proceeding: WNUT (ACL) 2021 11
Tasks: Japanese Word Segmentation, Lexical Normalization, Part-Of-Speech Tagging, POS, POS Tagging

Lexical normalization, in addition to word segmentation and part-of-speech tagging, is a fundamental task for Japanese user-generated text processing. In this paper, we propose a text editing model to solve the three task jointly and methods of pseudo-labeled data generation to overcome the problem of data deficiency. Our experiments showed that the proposed model achieved better normalization performance when trained on more diverse pseudo-labeled data.