pwc-archive/papers-with-abstracts huggingface dataset:
Automatically Cataloging Scholarly Articles using Library of Congress Subject Headings
Authors: Nazmul Kazi, Nathaniel Lane, Indika Kahanda
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Articles, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Institutes are required to catalog their articles with proper subject headings so that the users can easily retrieve relevant articles from the institutional repositories. However, due to the rate of proliferation of the number of articles in these repositories, it is becoming a challenge to manually catalog the newly added articles at the same pace. To address this challenge, we explore the feasibility of automatically annotating articles with Library of Congress Subject Headings (LCSH). We first use web scraping to extract keywords for a collection of articles from the Repository Analytics and Metrics Portal (RAMP). Then, we map these keywords to LCSH names for developing a gold-standard dataset. As a case study, using the subset of Biology-related LCSH concepts, we develop predictive models by formulating this task as a multi-label classification problem. Our experimental results demonstrate the viability of this approach for predicting LCSH for scholarly articles.
BERT meets Cranfield: Uncovering the Properties of Full Ranking on Fully Labeled Data
Authors: Negin Ghasemi, Djoerd Hiemstra
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Information Retrieval, Retrieval

Recently, various information retrieval models have been proposed based on pre-trained BERT models, achieving outstanding performance. The majority of such models have been tested on data collections with partial relevance labels, where various potentially relevant documents have not been exposed to the annotators. Therefore, evaluating BERT-based rankers may lead to biased and unfair evaluation results, simply because a relevant document has not been exposed to the annotators while creating the collection. In our work, we aim to better understand a BERT-based ranker{'}s strengths compared to a BERT-based re-ranker and the initial ranker. To this aim, we investigate BERT-based rankers performance on the Cranfield collection, which comes with full relevance judgment on all documents in the collection. Our results demonstrate the BERT-based full ranker{'}s effectiveness, as opposed to the BERT-based re-ranker and BM25. Also, analysis shows that there are documents that the BERT-based full-ranker finds that were not found by the initial ranker.
Siamese Neural Networks for Detecting Complementary Products
Authors: Marina Angelovska, Sina Sheikholeslami, Bas Dunn, Amir H. Payberah
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Recommendation Systems

Recommender systems play an important role in e-commerce websites as they improve the customer journey by helping the users find what they want at the right moment. In this paper, we focus on identifying a complementary relationship between the products of an e-commerce company. We propose a content-based recommender system for detecting complementary products, using Siamese Neural Networks (SNN). To this end, we implement and compare two different models: Siamese Convolutional Neural Network (CNN) and Siamese Long Short-Term Memory (LSTM). Moreover, we propose an extension of the SNN approach to handling millions of products in a matter of seconds, and we reduce the training time complexity by half. In the experiments, we show that Siamese LSTM can predict complementary products with an accuracy of {\textasciitilde}85{\%} using only the product titles.
Familiar words but strange voices: Modelling the influence of speech variability on word recognition
Authors: Alexandra Mayn, Badr M. Abdullah, Dietrich Klakow
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Form

We present a deep neural model of spoken word recognition which is trained to retrieve the meaning of a word (in the form of a word embedding) given its spoken form, a task which resembles that faced by a human listener. Furthermore, we investigate the influence of variability in speech signals on the model{'}s performance. To this end, we conduct of set of controlled experiments using word-aligned read speech data in German. Our experiments show that (1) the model is more sensitive to dialectical variation than gender variation, and (2) recognition performance of word cognates from related languages reflect the degree of relatedness between languages in our study. Our work highlights the feasibility of modeling human speech perception using deep neural networks.
Development of Conversational AI for Sleep Coaching Programme
Authors: Heereen Shim
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2

Almost 30{\%} of the adult population in the world is experiencing or has experience insomnia. Cognitive Behaviour Therapy for insomnia (CBT-I) is one of the most effective treatment, but it has limitations on accessibility and availability. Utilising technology is one of the possible solutions, but existing methods neglect conversational aspects, which plays a critical role in sleep therapy. To address this issue, we propose a PhD project exploring potentials of developing conversational artificial intelligence (AI) for a sleep coaching programme, which is motivated by CBT-I treatment. This PhD project aims to develop natural language processing (NLP) algorithms to allow the system to interact naturally with a user and provide automated analytic system to support human experts. In this paper, we introduce research questions lying under three phases of the sleep coaching programme: triaging, monitoring the progress, and providing coaching. We expect this research project{'}s outcomes could contribute to the research domains of NLP and AI but also the healthcare field by providing a more accessible and affordable sleep treatment solution and an automated analytic system to lessen the burden of human experts.
Relating Relations: Meta-Relation Extraction from Online Health Forum Posts
Authors: Daniel Stickley
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Relation, Relation Extraction

Relation extraction is a key task in knowledge extraction, and is commonly defined as the task of identifying relations that hold between entities in text. This thesis proposal addresses the specific task of identifying meta-relations, a higher order family of relations naturally construed as holding between other relations which includes temporal, comparative, and causal relations. More specifically, we aim to develop theoretical underpinnings and practical solutions for the challenges of (1) incorporating meta-relations into conceptualisations and annotation schemes for (lower-order) relations and named entities, (2) obtaining annotations for them with tolerable cognitive load on annotators, (3) creating models capable of reliably extracting meta-relations, and related to that (4) addressing the limited-data problem exacerbated by the introduction of meta-relations into the learning task. We explore recent works in relation extraction and discuss our plans to formally conceptualise meta-relations for the domain of user-generated health texts, and create a new dataset, annotation scheme and models for meta-relation extraction.
Towards Personalised and Document-level Machine Translation of Dialogue
Authors: Sebastian Vincent
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Document Level Machine Translation, Machine Translation, NMT, Sentence, Translation

State-of-the-art (SOTA) neural machine translation (NMT) systems translate texts at sentence level, ignoring context: intra-textual information, like the previous sentence, and extra-textual information, like the gender of the speaker. As a result, some sentences are translated incorrectly. Personalised NMT (PersNMT) and document-level NMT (DocNMT) incorporate this information into the translation process. Both fields are relatively new and previous work within them is limited. Moreover, there are no readily available robust evaluation metrics for them, which makes it difficult to develop better systems, as well as track global progress and compare different methods. This thesis proposal focuses on PersNMT and DocNMT for the domain of dialogue extracted from TV subtitles in five languages: English, Brazilian Portuguese, German, French and Polish. Three main challenges are addressed: (1) incorporating extra-textual information directly into NMT systems; (2) improving the machine translation of cohesion devices; (3) reliable evaluation for PersNMT and DocNMT.
Semantic-aware transformation of short texts using word embeddings: An application in the Food Computing domain
Authors: Andrea Morales-Garz{\'o}n, Juan G{\'o}mez-Romero, Maria J. Martin-Bautista
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Word Embeddings

Most works in food computing focus on generating new recipes from scratch. However, there is a large number of new online recipes generated daily with a large number of users reviews, with recommendations to improve the recipe flavor and ideas to modify them. This fact encourages the use of these data for obtaining improved and customized versions. In this thesis, we propose an adaptation engine based on fine-tuning a word embedding model. We will capture, in an unsupervised way, the semantic meaning of the recipe ingredients. We will use their word embedding representations to align them to external databases, thus enriching their data. The adaptation engine will use this food data to modify a recipe into another fitting specific user preferences (e.g., decrease caloric intake or make a recipe). We plan to explore different types of recipe adaptations while preserving recipe essential features such as cuisine style and essence simultaneously. We will also modify the rest of the recipe to the new changes to be reproducible.
Making Use of Latent Space in Language GANs for Generating Diverse Text without Pre-training
Authors: Takeshi Kojima, Yusuke Iwasawa, Yutaka Matsuo
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Diversity, Image Captioning, Text Generation

Generating diverse texts is an important factor for unsupervised text generation. One approach is to produce the diversity of texts conditioned by the sampled latent code. Although several generative adversarial networks (GANs) have been proposed thus far, these models still suffer from mode-collapsing if the models are not pre-trained. In this paper, we propose a GAN model that aims to improve the approach to generating diverse texts conditioned by the latent space. The generator of our model uses Gumbel-Softmax distribution for the word sampling process. To ensure that the text is generated conditioned upon the sampled latent code, reconstruction loss is introduced in our objective function. The discriminator of our model iteratively inspects incomplete partial texts and learns to distinguish whether they are real or fake by using the standard GAN objective function. Experimental results using the COCO Image Captions dataset show that, although our model is not pre-trained, the performance of our model is quite competitive with the existing baseline models, which requires pre-training.
Why Find the Right One?
Authors: Payal Khullar
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Machine Translation, NMT, Translation

The present paper investigates the impact of the anaphoric one words in English on the Neural Machine Translation (NMT) process using English-Hindi as source and target language pair. As expected, the experimental results show that the state-of-the-art Google English-Hindi NMT system achieves significantly poorly on sentences containing anaphoric ones as compared to the sentences containing regular, non-anaphoric ones. But, more importantly, we note that amongst the anaphoric words, the noun class is clearly much harder for NMT than the determinatives. This reaffirms the linguistic disparity of the two phenomenon in recent theoretical syntactic literature, despite the obvious surface similarities.
On the (In)Effectiveness of Images for Text Classification
Authors: Chunpeng Ma, Aili Shen, Hiyori Yoshikawa, Tomoya Iwakura, Daniel Beck, Timothy Baldwin
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Classification, text-classification, Text Classification

Images are core components of multi-modal learning in natural language processing (NLP), and results have varied substantially as to whether images improve NLP tasks or not. One confounding effect has been that previous NLP research has generally focused on sophisticated tasks (in varying settings), generally applied to English only. We focus on text classification, in the context of assigning named entity classes to a given Wikipedia page, where images generally complement the text and the Wikipedia page can be in one of a number of different languages. Our experiments across a range of languages show that images complement NLP models (including BERT) trained without external pre-training, but when combined with BERT models pre-trained on large-scale external data, images contribute nothing.
Keep Learning: Self-supervised Meta-learning for Learning from Inference
Authors: Akhil Kedia, Sai Chetan Chinthakindi
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Answer Selection, image-classification, Image Classification, Inductive Bias, Language Modelling, Meta-Learning, Question Answering, Self-Supervised Learning, Transfer Learning

A common approach in many machine learning algorithms involves self-supervised learning on large unlabeled data before fine-tuning on downstream tasks to further improve performance. A new approach for language modelling, called dynamic evaluation, further fine-tunes a trained model during inference using trivially-present ground-truth labels, giving a large improvement in performance. However, this approach does not easily extend to classification tasks, where ground-truth labels are absent during inference. We propose to solve this issue by utilizing self-training and back-propagating the loss from the model{'}s own class-balanced predictions (pseudo-labels), adapting the Reptile algorithm from meta-learning, combined with an inductive bias towards pre-trained weights to improve generalization. Our method improves the performance of standard backbones such as BERT, Electra, and ResNet-50 on a wide variety of tasks, such as question answering on SQuAD and NewsQA, benchmark task SuperGLUE, conversation response selection on Ubuntu Dialog corpus v2.0, as well as image classification on MNIST and ImageNet without any changes to the underlying models. Our proposed method outperforms previous approaches, enables self-supervised fine-tuning during inference of any classifier model to better adapt to target domains, can be easily adapted to any model, and is also effective in online and transfer-learning settings.
BERxiT: Early Exiting for BERT with Better Fine-Tuning and Extension to Regression
Authors: Ji Xin, Raphael Tang, YaoLiang Yu, Jimmy Lin
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: regression

The slow speed of BERT has motivated much research on accelerating its inference, and the early exiting idea has been proposed to make trade-offs between model quality and efficiency. This paper aims to address two weaknesses of previous work: (1) existing fine-tuning strategies for early exiting models fail to take full advantage of BERT; (2) methods to make exiting decisions are limited to classification tasks. We propose a more advanced fine-tuning strategy and a learning-to-exit module that extends early exiting to tasks other than classification. Experiments demonstrate improved early exiting for BERT, with better trade-offs obtained by the proposed fine-tuning strategy, successful application to regression tasks, and the possibility to combine it with other acceleration methods. Source code can be found at \url{https://github.com/castorini/berxit}.
On the Calibration and Uncertainty of Neural Learning to Rank Models for Conversational Search
Authors: Gustavo Penha, Claudia Hauff
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Conversational Search, Document Ranking, Learning-To-Rank, Retrieval

According to the Probability Ranking Principle (PRP), ranking documents in decreasing order of their probability of relevance leads to an optimal document ranking for ad-hoc retrieval. The PRP holds when two conditions are met: [C1] the models are well calibrated, and, [C2] the probabilities of relevance are reported with certainty. We know however that deep neural networks (DNNs) are often not well calibrated and have several sources of uncertainty, and thus [C1] and [C2] might not be satisfied by neural rankers. Given the success of neural Learning to Rank (LTR) approaches{---}and here, especially BERT-based approaches{---}we first analyze under which circumstances deterministic neural rankers are calibrated for conversational search problems. Then, motivated by our findings we use two techniques to model the uncertainty of neural rankers leading to the proposed stochastic rankers, which output a predictive distribution of relevance as opposed to point estimates. Our experimental results on the ad-hoc retrieval task of conversation response ranking reveal that (i) BERT-based rankers are not robustly calibrated and that stochastic BERT-based rankers yield better calibration; and (ii) uncertainty estimation is beneficial for both risk-aware neural ranking, i.e. taking into account the uncertainty when ranking documents, and for predicting unanswerable conversational contexts.
Maximal Multiverse Learning for Promoting Cross-Task Generalization of Fine-Tuned Language Models
Authors: Itzik Malkiel, Lior Wolf
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Language Modeling, Language Modelling, Unsupervised Pre-training

Language modeling with BERT consists of two phases of (i) unsupervised pre-training on unlabeled text, and (ii) fine-tuning for a specific supervised task. We present a method that leverages the second phase to its fullest, by applying an extensive number of parallel classifier heads, which are enforced to be orthogonal, while adaptively eliminating the weaker heads during training. We conduct an extensive inter- and intra-dataset evaluation, showing that our method improves the generalization ability of BERT, sometimes leading to a +9{\%} gain in accuracy. These results highlight the importance of a proper fine-tuning procedure, especially for relatively smaller-sized datasets. Our code is attached as supplementary.
Multi-split Reversible Transformers Can Enhance Neural Machine Translation
Authors: Yuekai Zhao, Shuchang Zhou, Zhihua Zhang
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Machine Translation, Translation

Large-scale transformers have been shown the state-of-the-art on neural machine translation. However, training these increasingly wider and deeper models could be tremendously memory intensive. We reduce the memory burden by employing the idea of reversible networks that a layer{'}s input can be reconstructed from its output. We design three types of multi-split based reversible transformers. We also devise a corresponding backpropagation algorithm, which does not need to store activations for most layers. Furthermore, we present two fine-tuning techniques: splits shuffle and self ensemble, to boost translation accuracy. Specifically, our best models surpass the vanilla transformer by at least 1.4 BLEU points in three datasets. Our large-scale reversible models achieve 30.0 BLEU in WMT{'}14 En-De and 43.5 BLEU in WMT{'}14 En-Fr, beating several very strong baselines with less than half of the training memory.
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference
Authors: Timo Schick, Hinrich Sch{\"u}tze
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Few-Shot Text Classification, Language Modeling, Language Modelling, Natural Language Inference, text-classification, Text Classification

Some NLP tasks can be solved in a fully unsupervised fashion by providing a pretrained language model with {``}task descriptions{''} in natural language (e.g., Radford et al., 2019). While this approach underperforms its supervised counterpart, we show in this work that the two ideas can be combined: We introduce Pattern-Exploiting Training (PET), a semi-supervised training procedure that reformulates input examples as cloze-style phrases to help language models understand a given task. These phrases are then used to assign soft labels to a large set of unlabeled examples. Finally, standard supervised training is performed on the resulting training set. For several tasks and languages, PET outperforms supervised training and strong semi-supervised approaches in low-resource settings by a large margin.
CD\^2CR: Co-reference resolution across documents and domains
Authors: James Ravenscroft, Amanda Clare, Arie Cattan, Ido Dagan, Maria Liakata
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Articles

Cross-document co-reference resolution (CDCR) is the task of identifying and linking mentions to entities and concepts across many text documents. Current state-of-the-art models for this task assume that all documents are of the same type (e.g. news articles) or fall under the same theme. However, it is also desirable to perform CDCR across different domains (type or theme). A particular use case we focus on in this paper is the resolution of entities mentioned across scientific work and newspaper articles that discuss them. Identifying the same entities and corresponding concepts in both scientific articles and news can help scientists understand how their work is represented in mainstream media. We propose a new task and English language dataset for cross-document cross-domain co-reference resolution (CD{\^{}}2CR). The task aims to identify links between entities across heterogeneous document types. We show that in this cross-domain, cross-document setting, existing CDCR models do not perform well and we provide a baseline model that outperforms current state-of-the-art CDCR models on CD{\^{}}2CR. Our data set, annotation tool and guidelines as well as our model for cross-document cross-domain co-reference are all supplied as open access open source resources.
``Talk to me with left, right, and angles'': Lexical entrainment in spoken Hebrew dialogue
Authors: Andreas Weise, Vered Silber-Varod, Anat Lerner, Julia Hirschberg, Rivka Levitan
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2

It has been well-documented for several languages that human interlocutors tend to adapt their linguistic productions to become more similar to each other. This behavior, known as entrainment, affects lexical choice as well, both with regard to specific words, such as referring expressions, and overall style. We offer what we believe to be the first investigation of such lexical entrainment in Hebrew. Using two existing measures, we analyze Hebrew speakers interacting in a Map Task, a popular experimental setup, and find rich evidence of lexical entrainment. Analyzing speaker pairs by the combination of their genders as well as speakers by their individual gender, we find no clear pattern of differences. We do, however, find that speakers in a position of less power entrain more than those with greater power, which matches theoretical accounts. Overall, our results mostly accord with those for American English, with a lack of entrainment on hedge words being the main difference.