pwc-archive/papers-with-abstracts huggingface dataset:
A Dataset for Linguistic Understanding, Visual Evaluation, and Recognition of Sign Languages: The K-RSL
Authors: Alfarabi Imashev, Medet Mukushev, Vadim Kimmelman, Anara Sandygulova
Date: 2020-11-01 00:00:00
Proceeding: CONLL 2020
Tasks: Gesture Recognition, Sign Language Recognition

The paper presents the first dataset that aims to serve interdisciplinary purposes for the utility of computer vision community and sign language linguistics. To date, a majority of Sign Language Recognition (SLR) approaches focus on recognising sign language as a manual gesture recognition problem. However, signers use other articulators: facial expressions, head and body position and movement to convey linguistic information. Given the important role of non-manual markers, this paper proposes a dataset and presents a use case to stress the importance of including non-manual features to improve the recognition accuracy of signs. To the best of our knowledge no prior publicly available dataset exists that explicitly focuses on non-manual components responsible for the grammar of sign languages. To this end, the proposed dataset contains 28250 videos of signs of high resolution and quality, with annotation of manual and non-manual components. We conducted a series of evaluations in order to investigate whether non-manual components would improve signs{'} recognition accuracy. We release the dataset to encourage SLR researchers and help advance current progress in this area toward real-time sign language interpretation. Our dataset will be made publicly available at https://krslproject.github.io/krsl-corpus
How well does surprisal explain N400 amplitude under different experimental conditions?
Authors: James Michaelov, Benjamin Bergen
Date: 2020-11-01 00:00:00
Proceeding: CONLL 2020

We investigate the extent to which word surprisal can be used to predict a neural measure of human language processing difficulty{---}the N400. To do this, we use recurrent neural networks to calculate the surprisal of stimuli from previously published neurolinguistic studies of the N400. We find that surprisal can predict N400 amplitude in a wide range of cases, and the cases where it cannot do so provide valuable insight into the neurocognitive processes underlying the response.
Towards a Task-Agnostic Model of Difficulty Estimation for Supervised Learning Tasks
Authors: Antonio Laverghetta Jr., Jamshidbek Mirzakhalov, John Licato
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Natural Language Inference

Curriculum learning, a training strategy where training data are ordered based on their difficulty, has been shown to improve performance and reduce training time on various NLP tasks. While much work over the years has developed novel approaches for generating curricula, these strategies are typically only suited for the task they were designed for. This work explores developing a task-agnostic model for problem difficulty and applying it to the Stanford Natural Language Inference (SNLI) dataset. Using the human responses that come with the dev set of SNLI, we train both regression and classification models to predict how many annotators will answer a question correctly and then project the difficulty estimates onto the full SNLI train set to create the curriculum. We argue that our curriculum is effectively capturing difficulty for this task through various analyses of both the model and the predicted difficulty scores.
A Siamese CNN Architecture for Learning Chinese Sentence Similarity
Authors: Haoxiang Shi, Cen Wang, Tetsuya Sakai
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Semantic Similarity, Semantic Textual Similarity, Sentence, Sentence Similarity

This paper presents a deep neural architecture which applies the siamese convolutional neural network sharing model parameters for learning a semantic similarity metric between two sentences. In addition, two different similarity metrics (i.e., the Cosine Similarity and Manhattan similarity) are compared based on this architecture. Our experiments in binary similarity classification for Chinese sentence pairs show that the proposed siamese convolutional architecture with Manhattan similarity outperforms the baselines (i.e., the siamese Long Short-Term Memory architecture and the siamese Bidirectional Long Short-Term Memory architecture) by 8.7 points in accuracy.
Automatic Classification of Students on Twitter Using Simple Profile Information
Authors: Lili-Michal Wilson, Christopher Wun
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Age Classification, Classification

Obtaining social media demographic information using machine learning is important for efficient computational social science research. Automatic age classification has been accomplished with relative success and allows for the study of youth populations, but student classification{---}determining which users are currently attending an academic institution{---}has not been thoroughly studied. Previous work (He et al., 2016) proposes a model which utilizes 3 tweet-content features to classify users as students or non-students. This model achieves an accuracy of 84{\%}, but is restrictive and time intensive because it requires accessing and processing many user tweets. In this study, we propose classification models which use 7 numerical features and 10 text-based features drawn from simple profile information. These profile-based features allow for faster, more accessible data collection and enable the classification of users without needing access to their tweets. Compared to previous models, our models identify students with greater accuracy; our best model obtains an accuracy of 88.1{\%} and an F1 score of .704. This improved student identification tool has the potential to facilitate research on topics ranging from professional networking to the impact of education on Twitter behaviors.
Hindi History Note Generation with Unsupervised Extractive Summarization
Authors: Aayush Shah, Dhineshkumar Ramasubbu, Dhruv Mathew, Meet Chetan Gadoya
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Document Summarization, Extractive Summarization, Unsupervised Extractive Summarization

In this work, the task of extractive single document summarization applied to an education setting to generate summaries of chapters from grade 10 Hindi history textbooks is undertaken. Unsupervised approaches to extract summaries are employed and evaluated. TextRank, LexRank, Luhn and KLSum are used to extract summaries. When evaluated intrinsically, Luhn and TextRank summaries have the highest ROUGE scores. When evaluated extrinsically, the effective measure of a summary in answering exam questions, TextRank summaries performs the best.
Unbiasing Review Ratings with Tendency Based Collaborative Filtering
Authors: Pranshi Yadav, Priya Yadav, Pegah Nokhiz, Vivek Gupta
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Collaborative Filtering, Prediction, Recommendation Systems

User-generated contents{'} score-based prediction and item recommendation has become an inseparable part of the online recommendation systems. The ratings allow people to express their opinions and may affect the market value of items and consumer confidence in e-commerce decisions. A major problem with the models designed for user review prediction is that they unknowingly neglect the rating bias occurring due to personal user bias preferences. We propose a tendency-based approach that models the user and item tendency for score prediction along with text review analysis with respect to ratings.
Building a Part-of-Speech Tagged Corpus for Drenjongke (Bhutia)
Authors: Mana Ashida, Seunghun Lee, Kunzang Namgyal
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Optical Character Recognition (OCR), POS

This research paper reports on the generation of the first Drenjongke corpus based on texts taken from a phrase book for beginners, written in the Tibetan script. A corpus of sentences was created after correcting errors in the text scanned through optical character reading (OCR). A total of 34 Part-of-Speech (PoS) tags were defined based on manual annotation performed by the three authors, one of whom is a native speaker of Drenjongke. The first corpus of the Drenjongke language comprises 275 sentences and 1379 tokens, which we plan to expand with other materials to promote further studies of this language.
Towards a Standardized Dataset on Indonesian Named Entity Recognition
Authors: Siti Oryza Khairunnisa, Aizhan Imankulova, Mamoru Komachi
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, TAG

In recent years, named entity recognition (NER) tasks in the Indonesian language have undergone extensive development. There are only a few corpora for Indonesian NER; hence, recent Indonesian NER studies have used diverse datasets. Although an open dataset is available, it includes only approximately 2,000 sentences and contains inconsistent annotations, thereby preventing accurate training of NER models without reliance on pre-trained models. Therefore, we re-annotated the dataset and compared the two annotations{'} performance using the Bidirectional Long Short-Term Memory and Conditional Random Field (BiLSTM-CRF) approach. Fixing the annotation yielded a more consistent result for the organization tag and improved the prediction score by a large margin. Moreover, to take full advantage of pre-trained models, we compared different feature embeddings to determine their impact on the NER task for the Indonesian language.
Formal Sanskrit Syntax: A Specification for Programming Language
Authors: K. Kabi Khanganba, Girish Jha
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020

The paper discusses the syntax of the primary statements of the Sanskritam, a programming language specification based on natural Sanskrit under a doctoral thesis. By a statement, we mean a syntactic unit regardless of its computational operations of variable declarations, program executions or evaluations of Boolean expressions etc. We have selected six common primary statements of declaration, assignment, inline initialization, if-then-else, for loop and while loop. The specification partly overlaps the ideas of natural language programming, Controlled Natural Language (Kunh, 2013), and Natural Language subset. The practice and application of structured natural language set in a discourse are deeply rooted in the theoretical text tradition of Sanskrit, like the s{\=u}tra-based disciplines and Navya-Ny{\=a}ya (NN) formal language, etc. The effort is a kind of continuation and application of such traditions and their techniques in the modern field of Sanskrit NLP.
Resource Creation and Evaluation of Aspect Based Sentiment Analysis in Urdu
Authors: Sadaf Rani, Muhammad Waqas Anwar
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis

Along with the rise of people generated content on social sites, sentiment analysis has gained more importance. Aspect Based Sentiment Analysis (ABSA) is a task of identifying the sentiment at aspect level. It has more importance than sentiment analysis from commercial point of view. To the best of our knowledge, there is very few work on ABSA in Urdu language. Recent work on ABSA has limitations. Only predefined aspects are identified in a specific domain. So our focus is on the creation and evaluation of dataset for ABSA in Urdu language which will support multiple aspects. This dataset will provide a baseline evaluation for ABSA systems.
Making a Point: Pointer-Generator Transformers for Disjoint Vocabularies
Authors: Nikhil Prabhu, Katharina Kann
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Grapheme-to-Phoneme Conversion

Explicit mechanisms for copying have improved the performance of neural models for sequence-to-sequence tasks in the low-resource setting. However, they rely on an overlap between source and target vocabularies. Here, we propose a model that does not: a pointer-generator transformer for disjoint vocabularies. We apply our model to a low-resource version of the grapheme-to-phoneme conversion (G2P) task, and show that it outperforms a standard transformer by an average of 5.1 WER over 15 languages. While our model does not beat the the best performing baseline, we demonstrate that it provides complementary information to it: an oracle that combines the best outputs of the two models improves over the strongest baseline by 7.7 WER on average in the low-resource setting. In the high-resource setting, our model performs comparably to a standard transformer.
Training with Adversaries to Improve Faithfulness of Attention in Neural Machine Translation
Authors: Pooya Moradi, Nishant Kambhatla, Anoop Sarkar
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Machine Translation, NMT, Translation

Can we trust that the attention heatmaps produced by a neural machine translation (NMT) model reflect its true internal reasoning? We isolate and examine in detail the notion of faithfulness in NMT models. We provide a measure of faithfulness for NMT based on a variety of stress tests where model parameters are perturbed and measuring faithfulness based on how often the model output changes. We show that our proposed faithfulness measure for NMT models can be improved using a novel differentiable objective that rewards faithful behaviour by the model through probability divergence. Our experimental results on multiple language pairs show that our objective function is effective in increasing faithfulness and can lead to a useful analysis of NMT model behaviour and more trustworthy attention heatmaps. Our proposed objective improves faithfulness without reducing the translation quality and it also seems to have a useful regularization effect on the NMT model and can even improve translation quality in some cases.
Document-Level Neural Machine Translation Using BERT as Context Encoder
Authors: Zhiyu Guo, Minh Le Nguyen
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Decoder, Document Level Machine Translation, Machine Translation, Natural Language Understanding, Sentence, Translation

Large-scale pre-trained representations such as BERT have been widely used in many natural language understanding tasks. The methods of incorporating BERT into document-level machine translation are still being explored. BERT is able to understand sentence relationships since BERT is pre-trained using the next sentence prediction task. In our work, we leverage this property to improve document-level machine translation. In our proposed model, BERT performs as a context encoder to achieve document-level contextual information, which is then integrated into both the encoder and decoder. Experiment results show that our proposed method can significantly outperform strong document-level machine translation baselines on BLEU score. Moreover, the ablation study shows our method can capture document-level context information to boost translation performance.
A Review of Cross-Domain Text-to-SQL Models
Authors: Yujian Gan, Matthew Purver, John R. Woodward
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Text to SQL, Text-To-SQL, Word Embeddings

WikiSQL and Spider, the large-scale cross-domain text-to-SQL datasets, have attracted much attention from the research community. The leaderboards of WikiSQL and Spider show that many researchers propose their models trying to solve the text-to-SQL problem. This paper first divides the top models in these two leaderboards into two paradigms. We then present details not mentioned in their original paper by evaluating the key components, including schema linking, pretrained word embeddings, and reasoning assistance modules. Based on the analysis of these models, we want to promote understanding of the text-to-SQL field and find out some interesting future works, for example, it is worth studying the text-to-SQL problem in an environment where it is more challenging to build schema linking and also worth studying combing the advantage of each model toward text-to-SQL.
Multi-task Learning for Automated Essay Scoring with Sentiment Analysis
Authors: Panitan Muangkammuen, Fumiyo Fukumoto
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Automated Essay Scoring, Multi-Task Learning, Sentence, Sentiment Analysis

Automated Essay Scoring (AES) is a process that aims to alleviate the workload of graders and improve the feedback cycle in educational systems. Multi-task learning models, one of the deep learning techniques that have recently been applied to many NLP tasks, demonstrate the vast potential for AES. In this work, we present an approach for combining two tasks, sentiment analysis, and AES by utilizing multi-task learning. The model is based on a hierarchical neural network that learns to predict a holistic score at the document-level along with sentiment classes at the word-level and sentence-level. The sentiment features extracted from opinion expressions can enhance a vanilla holistic essay scoring, which mainly focuses on lexicon and text semantics. Our approach demonstrates that sentiment features are beneficial for some essay prompts, and the performance is competitive to other deep learning models on the Automated StudentAssessment Prize (ASAP) benchmark. TheQuadratic Weighted Kappa (QWK) is used to measure the agreement between the human grader{'}s score and the model{'}s prediction. Ourmodel produces a QWK of 0.763.
Aspect Extraction Using Coreference Resolution and Unsupervised Filtering
Authors: Deon Mai, Wei Emma Zhang
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Extraction, coreference-resolution, Coreference Resolution, Sentiment Analysis

Aspect extraction is a widely researched field of natural language processing in which aspects are identified from the text as a means for information. For example, in aspect-based sentiment analysis (ABSA), aspects need to be first identified. Previous studies have introduced various approaches to increasing accuracy, although leaving room for further improvement. In a practical situation where the examined dataset is lacking labels, to fine-tune the process a novel unsupervised approach is proposed, combining a lexical rule-based approach with coreference resolution. The model increases accuracy through the recognition and removal of coreferring aspects. Experimental evaluations are performed on two benchmark datasets, demonstrating the greater performance of our approach to extracting coherent aspects through outperforming the baseline approaches.
GRUBERT: A GRU-Based Method to Fuse BERT Hidden Layers for Twitter Sentiment Analysis
Authors: Leo Horne, Matthias Matti, Pouya Pourjafar, Zuowen Wang
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Sentiment Analysis, Twitter Sentiment Analysis

In this work, we introduce a GRU-based architecture called GRUBERT that learns to map the different BERT hidden layers to fused embeddings with the aim of achieving high accuracy on the Twitter sentiment analysis task. Tweets are known for their highly diverse language, and by exploiting different linguistic information present across BERT hidden layers, we can capture the full extent of this language at the embedding level. Our method can be easily adapted to other embeddings capturing different linguistic information. We show that our method outperforms well-known heuristics of using BERT (e.g. using only the last layer) and other embeddings such as ELMo. We observe potential label noise resulting from the data acquisition process and employ early stopping as well as a voting classifier to overcome it.
Exploring Statistical and Neural Models for Noun Ellipsis Detection and Resolution in English
Authors: Payal Khullar
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020

Computational approaches to noun ellipsis resolution has been sparse, with only a naive rule-based approach that uses syntactic feature constraints for marking noun ellipsis licensors and selecting their antecedents. In this paper, we further the ellipsis research by exploring several statistical and neural models for both the subtasks involved in the ellipsis resolution process and addressing the representation and contribution of manual features proposed in previous research. Using the best performing models, we build an end-to-end supervised Machine Learning (ML) framework for this task that improves the existing F1 score by 16.55{\%} for the detection and 14.97{\%} for the resolution subtask. Our experiments demonstrate robust scores through pretrained BERT (Bidirectional Encoder Representations from Transformers) embeddings for word representation, and more so the importance of manual features{--} once again highlighting the syntactic and semantic characteristics of the ellipsis phenomenon. For the classification decision, we notice that a simple Multilayar Perceptron (MLP) works well for the detection of ellipsis; however, Recurrent Neural Networks (RNN) are a better choice for the much harder resolution step.
MRC Examples Answerable by BERT without a Question Are Less Effective in MRC Model Training
Authors: Hongyu Li, Tengyang Chen, Shuting Bai, Takehito Utsuro, Yasuhide Kawada
Date: 2020-12-01 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Machine Reading Comprehension, Reading Comprehension

Models developed for Machine Reading Comprehension (MRC) are asked to predict an answer from a question and its related context. However, there exist cases that can be correctly answered by an MRC model using BERT, where only the context is provided without including the question. In this paper, these types of examples are referred to as {``}easy to answer{''}, while others are as {``}hard to answer{''}, i.e., unanswerable by an MRC model using BERT without being provided the question. Based on classifying examples as answerable or unanswerable by BERT without the given question, we propose a method based on BERT that splits the training examples from the MRC dataset SQuAD1.1 into those that are {``}easy to answer{''} or {``}hard to answer{''}. Experimental evaluation from a comparison of two models, one trained only with {``}easy to answer{''} examples and the other with {``}hard to answer{''} examples demonstrates that the latter outperforms the former.