pwc-archive/papers-with-abstracts huggingface dataset:
NIT Rourkela Machine Translation(MT) System Submission to WAT 2022 for MultiIndicMT: An Indic Language Multilingual Shared Task
Authors: Sudhansu Bala Das, Atharv Biradar, Tapas Kumar Mishra, Bidyut Kumar Patra
Date: 2022-10-01 00:00:00
Proceeding: WAT 2022 10
Tasks: Decoder, Machine Translation, NMT, Translation

Multilingual Neural Machine Translation (MNMT) exhibits incredible performance with the development of a single translation model for many languages. Previous studies on multilingual translation reveal that multilingual training is effective for languages with limited corpus. This paper presents our submission (Team Id: NITR) in the WAT 2022 for “MultiIndicMT shared task” where the objective of the task is the translation between 5 Indic languages from OPUS Corpus (which are newly added in WAT 2022 corpus) into English and vice versa using the corpus provided by the organizer of WAT. Our system is based on a transformer-based NMT using fairseq modelling toolkit with ensemble techniques. Heuristic pre-processing approaches are carried out before keeping the model under training. Our multilingual NMT systems are trained with shared encoder and decoder parameters followed by assigning language embeddings to each token in both encoder and decoder. Our final multilingual system was examined by using BLEU and RIBES metrics scores. In future, we look forward to extend our research that will help in fine-tuning of both encoder and decoder during the monolingual unsupervised training in order to improve the quality of the synthetic data generated during the process.
Investigation of Multilingual Neural Machine Translation for Indian Languages
Authors: Sahinur Rahman Laskar, Riyanka Manna, Partha Pakray, Sivaji Bandyopadhyay
Date: 2022-10-01 00:00:00
Proceeding: WAT 2022 10
Tasks: Machine Translation, Translation, Transliteration

In the domain of natural language processing, machine translation is a well-defined task where one natural language is automatically translated to another natural language. The deep learning-based approach of machine translation, known as neural machine translation attains remarkable translational performance. However, it requires a sufficient amount of training data which is a critical issue for low-resource pair translation. To handle the data scarcity problem, the multilingual concept has been investigated in neural machine translation in different settings like many-to-one and one-to-many translation. WAT2022 (Workshop on Asian Translation 2022) organizes (hosted by the COLING 2022) Indic tasks: English-to-Indic and Indic-to-English translation tasks where we have participated as a team named CNLP-NITS-PP. Herein, we have investigated a transliteration-based approach, where Indic languages are transliterated into English script and shared sub-word level vocabulary during the training phase. We have attained BLEU scores of 2.0 (English-to-Bengali), 1.10 (English-to-Assamese), 4.50 (Bengali-to-English), and 3.50 (Assamese-to-English) translation, respectively.
Does partial pretranslation can improve low ressourced-languages pairs?
Authors: Raoul Blin
Date: 2022-10-01 00:00:00
Proceeding: WAT 2022 10
Tasks: Translation, Word Translation

We study the effects of a local and punctual pretranslation of the source corpus on the performance of a Transformer translation model. The pretranslations are performed at the morphological (morpheme translation), lexical (word translation) and morphosyntactic (numeral groups and dates) levels. We focus on small and medium-sized training corpora (50K ~ 2.5M bisegments) and on a linguistically distant language pair (Japanese and French). We find that this type of pretranslation does not lead to significant progress. We describe the motivations of the approach, the specific difficulties of Japanese-French translation. We discuss the possible reasons for the observed underperformance.
Silo NLP’s Participation at WAT2022
Authors: Shantipriya Parida, Subhadarshi Panda, Stig-Arne Grönroos, Mark Granroth-Wilding, Mika Koistinen
Date: 2022-10-01 00:00:00
Proceeding: WAT 2022 10
Tasks: Translation

This paper provides the system description of “Silo NLP’s” submission to the Workshop on Asian Translation (WAT2022). We have participated in the Indic Multimodal tasks (English->Hindi, English->Malayalam, and English->Bengali, Multimodal Translation). For text-only translation, we used the Transformer and fine-tuned the mBART. For multimodal translation, we used the same architecture and extracted object tags from the images to use as visual features concatenated with the text sequence for input. Our submission tops many tasks including English->Hindi multimodal translation (evaluation test), English->Malayalam text-only and multimodal translation (evaluation test), English->Bengali multimodal translation (challenge test), and English->Bengali text-only translation (evaluation test).
PICT@WAT 2022: Neural Machine Translation Systems for Indic Languages
Authors: Anupam Patil, Isha Joshi, Dipali Kadam
Date: 2022-10-01 00:00:00
Proceeding: WAT 2022 10
Tasks: Machine Translation, Translation

Translation entails more than simply translating words from one language to another. It is vitally essential for effective cross-cultural communication, thus making good translation systems an important requirement. We describe our systems in this paper, which were submitted to the WAT 2022 translation shared tasks. As part of the Multi-modal translation tasks’ text-only translation sub-tasks, we submitted three Neural Machine Translation systems based on Transformer models for English to Malayalam, English to Bengali, and English to Hindi text translation. We found significant results on the leaderboard for English-Indic (en-xx) systems utilizing BLEU and RIBES scores as comparative metrics in our studies. For the respective translations of English to Malayalam, Bengali, and Hindi, we obtained BLEU scores of 19.50, 32.90, and 41.80 for the challenge subset and 30.60, 39.80, and 42.90 on the benchmark evaluation subset data.
English to Bengali Multimodal Neural Machine Translation using Transliteration-based Phrase Pairs Augmentation
Authors: Sahinur Rahman Laskar, Pankaj Dadure, Riyanka Manna, Partha Pakray, Sivaji Bandyopadhyay
Date: 2022-10-01 00:00:00
Proceeding: WAT 2022 10
Tasks: Machine Translation, NMT, Translation, Transliteration

Automatic translation of one natural language to another is a popular task of natural language processing. Although the deep learning-based technique known as neural machine translation (NMT) is a widely accepted machine translation approach, it needs an adequate amount of training data, which is a challenging issue for low-resource pair translation. Moreover, the multimodal concept utilizes text and visual features to improve low-resource pair translation. WAT2022 (Workshop on Asian Translation 2022) organizes (hosted by the COLING 2022) English to Bengali multimodal translation task where we have participated as a team named CNLP-NITS-PP in two tracks: 1) text-only and 2) multimodal translation. Herein, we have proposed a transliteration-based phrase pairs augmentation approach which shows improvement in the multimodal translation task and achieved benchmark results on Bengali Visual Genome 1.0 dataset. We have attained the best results on the challenge and evaluation test set for English to Bengali multimodal translation with BLEU scores of 28.70, 43.90 and RIBES scores of 0.688931, 0.780669, respectively.
Investigation of English to Hindi Multimodal Neural Machine Translation using Transliteration-based Phrase Pairs Augmentation
Authors: Sahinur Rahman Laskar, Rahul Singh, Md Faizal Karim, Riyanka Manna, Partha Pakray, Sivaji Bandyopadhyay
Date: 2022-10-01 00:00:00
Proceeding: WAT 2022 10
Tasks: Machine Translation, NMT, Translation, Transliteration

Machine translation translates one natural language to another, a well-defined natural language processing task. Neural machine translation (NMT) is a widely accepted machine translation approach, but it requires a sufficient amount of training data, which is a challenging issue for low-resource pair translation. Moreover, the multimodal concept utilizes text and visual features to improve low-resource pair translation. WAT2022 (Workshop on Asian Translation 2022) organizes (hosted by the COLING 2022) English to Hindi multimodal translation task where we have participated as a team named CNLP-NITS-PP in two tracks: 1) text-only and 2) multimodal translation. Herein, we have proposed a transliteration-based phrase pairs augmentation approach, which shows improvement in the multimodal translation task. We have attained the second best results on the challenge test set for English to Hindi multimodal translation with BLEU score of 39.30, and a RIBES score of 0.791468.
Online Neural Coreference Resolution with Rollback
Authors: Patrick Xia, Benjamin Van Durme
Date: 2022-10-01 00:00:00
Proceeding: COLING (CRAC) 2022 10
Tasks: coreference-resolution, Coreference Resolution, Dialogue Understanding

Humans process natural language online, whether reading a document or participating in multiparty dialogue. Recent advances in neural coreference resolution have focused on offline approaches that assume the full communication history as input. This is neither realistic nor sufficient if we wish to support dialogue understanding in real-time. We benchmark two existing, offline, models and highlight their shortcomings in the online setting. We then modify these models to perform online inference and introduce rollback: a short-term mechanism to correct mistakes. We demonstrate across five English datasets the effectiveness of this approach against an offline and a naive online model in terms of latency, final document-level coreference F1, and average running F1.
Analyzing Coreference and Bridging in Product Reviews
Authors: Hideo Kobayashi, Christopher Malon
Date: 2022-10-01 00:00:00
Proceeding: COLING (CRAC) 2022 10
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Opinion Summarization, Sentiment Analysis

Product reviews may have complex discourse including coreference and bridging relations to a main product, competing products, and interacting products. Current approaches to aspect-based sentiment analysis (ABSA) and opinion summarization largely ignore this complexity. On the other hand, existing systems for coreference and bridging were trained in a different domain. We collect mention type annotations relevant to coreference and bridging for 498 product reviews. Using these annotations, we show that a state-of-the-art factuality score fails to catch coreference errors in product reviews, and that a state-of-the-art coreference system trained on OntoNotes does not perform nearly as well on product mentions. As our dataset grows, we expect it to help ABSA and opinion summarization systems to avoid entity reference errors.
Anaphoric Phenomena in Situated dialog: A First Round of Annotations
Authors: Sharid Loáiciga, Simon Dobnik, David Schlangen
Date: 2022-10-01 00:00:00
Proceeding: COLING (CRAC) 2022 10

We present a first release of 500 documents from the multimodal corpus Tell-me-more (Ilinykh et al., 2019) annotated with coreference information according to the ARRAU guidelines (Poesio et al., 2021). The corpus consists of images and short texts of five sentences. We describe the annotation process and present the adaptations to the original guidelines in order to account for the challenges of grounding the annotations to the image. 50 documents from the 500 available are annotated by two people and used to estimate inter-annotator agreement (IAA) relying on Krippendorff’s alpha.
Building a Manually Annotated Hungarian Coreference Corpus: Workflow and Tools
Authors: Noémi Vadász
Date: 2022-10-01 00:00:00
Proceeding: COLING (CRAC) 2022 10

This paper presents the complete workflow of building a manually annotated Hungarian corpus, KorKor, with particular reference to anaphora and coreference annotation. All linguistic annotation layers were corrected manually. The corpus is freely available in two formats. The paper gives insight into the process of setting up the workflow and the challenges that have arisen.
NARC – Norwegian Anaphora Resolution Corpus
Authors: Petter Mæhlum, Dag Haug, Tollef Jørgensen, Andre Kåsen, Anders Nøklestad, Egil Rønningstad, Per Erik Solberg, Erik Velldal, Lilja Øvrelid
Date: 2022-10-01 00:00:00
Proceeding: COLING (CRAC) 2022 10
Tasks: Relation

We present the Norwegian Anaphora Resolution Corpus (NARC), the first publicly available corpus annotated with anaphoric relations between noun phrases for Norwegian. The paper describes the annotated data for 326 documents in Norwegian Bokmål, together with inter-annotator agreement and discussions of relevant statistics. We also present preliminary modelling results which are comparable to existing corpora for other languages, and discuss relevant problems in relation to both modelling and the annotations themselves.
Evaluating Coreference Resolvers on Community-based Question Answering: From Rule-based to State of the Art
Authors: Haixia Chai, Nafise Sadat Moosavi, Iryna Gurevych, Michael Strube
Date: 2022-10-01 00:00:00
Proceeding: COLING (CRAC) 2022 10
Tasks: Answer Selection, coreference-resolution, Coreference Resolution, Natural Language Understanding, Question Answering

Coreference resolution is a key step in natural language understanding. Developments in coreference resolution are mainly focused on improving the performance on standard datasets annotated for coreference resolution. However, coreference resolution is an intermediate step for text understanding and it is not clear how these improvements translate into downstream task performance. In this paper, we perform a thorough investigation on the impact of coreference resolvers in multiple settings of community-based question answering task, i.e., answer selection with long answers. Our settings cover multiple text domains and encompass several answer selection methods. We first inspect extrinsic evaluation of coreference resolvers on answer selection by using coreference relations to decontextualize individual sentences of candidate answers, and then annotate a subset of answers with coreference information for intrinsic evaluation. The results of our extrinsic evaluation show that while there is a significant difference between the performance of the rule-based system vs. state-of-the-art neural model on coreference resolution datasets, we do not observe a considerable difference on their impact on downstream models. Our intrinsic evaluation shows that (i) resolving coreference relations on less-formal text genres is more difficult even for trained annotators, and (ii) the values of linguistic-agnostic coreference evaluation metrics do not correlate with the impact on downstream data.
Improving Bridging Reference Resolution using Continuous Essentiality from Crowdsourcing
Authors: Nobuhiro Ueda, Sadao Kurohashi
Date: 2022-10-01 00:00:00
Proceeding: COLING (CRAC) 2022 10

Bridging reference resolution is the task of finding nouns that complement essential information of another noun. The essentiality varies depending on noun combination and context and has a continuous distribution. Despite the continuous nature of essentiality, existing datasets of bridging reference have only a few coarse labels to represent the essentiality. In this work, we propose a crowdsourcing-based annotation method that considers continuous essentiality. In the crowdsourcing task, we asked workers to select both all nouns with a bridging reference relation and a noun with the highest essentiality among them. Combining these annotations, we can obtain continuous essentiality. Experimental results demonstrated that the constructed dataset improves bridging reference resolution performance. The code is available at https://github.com/nobu-g/bridging-resolution.
Investigating Cross-Document Event Coreference for Dutch
Authors: Loic De Langhe, Orphee De Clercq, Veronique Hoste
Date: 2022-10-01 00:00:00
Proceeding: COLING (CRAC) 2022 10
Tasks: coreference-resolution, Coreference Resolution, Event Coreference Resolution

In this paper we present baseline results for Event Coreference Resolution (ECR) in Dutch using gold-standard (i.e non-predicted) event mentions. A newly developed benchmark dataset allows us to properly investigate the possibility of creating ECR systems for both within and cross-document coreference. We give an overview of the state of the art for ECR in other languages, as well as a detailed overview of existing ECR resources. Afterwards, we provide a comparative report on our own dataset. We apply a significant number of approaches that have been shown to attain good results for English ECR including feature-based models, monolingual transformer language models and multilingual language models. The best results were obtained using the monolingual BERTje model. Finally, results for all models are thoroughly analysed and visualised, as to provide insight into the inner workings of ECR and long-distance semantic NLP tasks in general.
The Role of Common Ground for Referential Expressions in Social Dialogues
Authors: Jaap Kruijt, Piek Vossen
Date: 2022-10-01 00:00:00
Proceeding: COLING (CRAC) 2022 10

In this paper, we frame the problem of co-reference resolution in dialogue as a dynamic social process in which mentions to people previously known and newly introduced are mixed when people know each other well. We restructured an existing data set for the Friends sitcom as a coreference task that evolves over time, where close friends make reference to other people either part of their common ground (inner circle) or not (outer circle). We expect that awareness of common ground is key in social dialogue in order to resolve references to the inner social circle, whereas local contextual information plays a more important role for outer circle mentions. Our analysis of these references confirms that there are differences in naming and introducing these people. We also experimented with the SpanBERT coreference system with and without fine-tuning to measure whether preceding discourse contexts matter for resolving inner and outer circle mentions. Our results show that more inner circle mentions lead to a decrease in model performance, and that fine-tuning on preceding contexts reduces false negatives for both inner and outer circle mentions but increases the false positives as well, showing that the models overfit on these contexts.
Coreference Resolution for Polish: Improvements within the CRAC 2022 Shared Task
Authors: Karol Saputa
Date: 2022-10-01 00:00:00
Proceeding: CRAC (ACL) 2022 10
Tasks: coreference-resolution, Coreference Resolution

The paper presents our system for coreference resolution in Polish. We compare the system with previous works for the Polish language as well as with the multilingual approach in the CRAC 2022 Shared Task on Multilingual Coreference Resolution thanks to a universal, multilingual data format and evaluation tool. We discuss the accuracy, computational performance, and evaluation approach of the new System which is a faster, end-to-end solution.
Changes in Tweet Geolocation over Time: A Study with Carmen 2.0
Authors: Jingyu Zhang, Alexandra DeLucia, Mark Dredze
Date: 2022-10-01 00:00:00
Proceeding: COLING (WNUT) 2022 10

Researchers across disciplines use Twitter geolocation tools to filter data for desired locations. These tools have largely been trained and tested on English tweets, often originating in the United States from almost a decade ago. Despite the importance of these tools for data curation, the impact of tweet language, country of origin, and creation date on tool performance remains largely unknown. We explore these issues with Carmen, a popular tool for Twitter geolocation. To support this study we introduce Carmen 2.0, a major update which includes the incorporation of GeoNames, a gazetteer that provides much broader coverage of locations. We evaluate using two new Twitter datasets, one for multilingual, multiyear geolocation evaluation, and another for usage trends over time. We found that language, country origin, and time does impact geolocation tool performance.
Cross-Dialect Social Media Dependency Parsing for Social Scientific Entity Attribute Analysis
Authors: Chloe Eggleston, Brendan O’Connor
Date: 2022-10-01 00:00:00
Proceeding: COLING (WNUT) 2022 10
Tasks: Attribute, Dependency Parsing

In this paper, we utilize recent advancements in social media natural language processing to obtain state-of-the-art syntactic dependency parsing results for social media English. We observe performance gains of 3.4 UAS and 4.0 LAS against the previous state-of-the-art as well as less disparity between African-American and Mainstream American English dialects. We demonstrate the computational social scientific utility of this parser for the task of socially embedded entity attribute analysis: for a specified entity, derive its semantic relationships from parses’ rich syntax, and accumulate and compare them across social variables. We conduct a case study on politicized views of U.S. official Anthony Fauci during the COVID-19 pandemic.
Impact of Environmental Noise on Alzheimer’s Disease Detection from Speech: Should You Let a Baby Cry?
Authors: Jekaterina Novikova
Date: 2022-10-01 00:00:00
Proceeding: COLING (WNUT) 2022 10

Research related to automatically detecting Alzheimer’s disease (AD) is important, given the high prevalence of AD and the high cost of traditional methods. Since AD significantly affects the acoustics of spontaneous speech, speech processing and machine learning (ML) provide promising techniques for reliably detecting AD. However, speech audio may be affected by different types of background noise and it is important to understand how the noise influences the accuracy of ML models detecting AD from speech. In this paper, we study the effect of fifteen types of environmental noise from five different categories on the performance of four ML models trained with three types of acoustic representations. We perform a thorough analysis showing how ML models and acoustic features are affected by different types of acoustic noise. We show that acoustic noise is not necessarily harmful - certain types of noise are beneficial for AD detection models and help increasing accuracy by up to 4.8%. We provide recommendations on how to utilize acoustic noise in order to achieve the best performance results with the ML models deployed in real world.
Exploring Multimodal Features and Fusion Strategies for Analyzing Disaster Tweets
Authors: Raj Pranesh
Date: 2022-10-01 00:00:00
Proceeding: COLING (WNUT) 2022 10
Tasks: Transfer Learning

Social media platforms, such as Twitter, often provide firsthand news during the outbreak of a crisis. It is extremely essential to process these facts quickly to plan the response efforts for minimal loss. Therefore, in this paper, we present an analysis of various multimodal feature fusion techniques to analyze and classify disaster tweets into multiple crisis events via transfer learning. In our study, we utilized three image models pre-trained on ImageNet dataset and three fine-tuned language models to learn the visual and textual features of the data and combine them to make predictions. We have presented a systematic analysis of multiple intra-modal and cross-modal fusion strategies and their effect on the performance of the multimodal disaster classification system. In our experiment, we used 8,242 disaster tweets, each comprising image, and text data with five disaster event classes. The results show that the multimodal with transformer-attention mechanism and factorized bilinear pooling (FBP) for intra-modal and cross-modal feature fusion respectively achieved the best performance.