pwc-archive/papers-with-abstracts huggingface dataset:
Be Different to Be Better! A Benchmark to Leverage the Complementarity of Language and Vision
Authors: Sandro Pezzelle, Claudio Greco, Greta Gandolfi, Eleonora Gualdoni, Raffaella Bernardi
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020

This paper introduces BD2BB, a novel language and vision benchmark that requires multimodal models combine complementary information from the two modalities. Recently, impressive progress has been made to develop universal multimodal encoders suitable for virtually any language and vision tasks. However, current approaches often require them to combine redundant information provided by language and vision. Inspired by real-life communicative contexts, we propose a novel task where either modality is necessary but not sufficient to make a correct prediction. To do so, we first build a dataset of images and corresponding sentences provided by human participants. Second, we evaluate state-of-the-art models and compare their performance against human speakers. We show that, while the task is relatively easy for humans, best-performing models struggle to achieve similar results.
Cross-Lingual Training of Neural Models for Document Ranking
Authors: Peng Shi, He Bai, Jimmy Lin
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Document Ranking, Retrieval

We tackle the challenge of cross-lingual training of neural document ranking models for mono-lingual retrieval, specifically leveraging relevance judgments in English to improve search in non-English languages. Our work successfully applies multi-lingual BERT (mBERT) to document ranking and additionally compares against a number of alternatives: translating the training data, translating documents, multi-stage hybrids, and ensembles. Experiments on test collections in six different languages from diverse language families reveal many interesting findings: model-based relevance transfer using mBERT can significantly improve search quality in (non-English) mono-lingual retrieval, but other {``}low resource{''} approaches are competitive as well.
LSTMs Compose---and Learn---Bottom-Up
Authors: Naomi Saphra, Adam Lopez
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020

Recent work in NLP shows that LSTM language models capture compositional structure in language data. In contrast to existing work, we consider the \textit{learning} process that leads to compositional behavior. For a closer look at how an LSTM{'}s sequential representations are composed hierarchically, we present a related measure of Decompositional Interdependence (DI) between word meanings in an LSTM, based on their gate interactions. We support this measure with experiments on English language data, where DI is higher on pairs of words with lower syntactic distance. To explore the inductive biases that cause these compositional representations to arise during training, we conduct simple experiments on synthetic data. These synthetic experiments support a specific hypothesis about how hierarchical structures are discovered over the course of training: that LSTM constituent representations are learned bottom-up, relying on effective representations of their shorter children, rather than on learning the longer-range relations independently.
Can Pre-training help VQA with Lexical Variations?
Authors: Shailza Jolly, Shubham Kapoor
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Rephrasings or paraphrases are sentences with similar meanings expressed in different ways. Visual Question Answering (VQA) models are closing the gap with the oracle performance for datasets like VQA2.0. However, these models fail to perform well on rephrasings of a question, which raises some important questions like Are these models robust towards linguistic variations? Is it the architecture or the dataset that we need to optimize? In this paper, we analyzed VQA models in the space of paraphrasing. We explored the role of language {\&} cross-modal pre-training to investigate the robustness of VQA models towards lexical variations. Our experiments find that pre-trained language encoders generate efficient representations of question rephrasings, which help VQA models correctly infer these samples. We empirically determine why pre-training language encoders improve lexical robustness. Finally, we observe that although pre-training all VQA components obtain state-of-the-art results on the VQA-Rephrasings dataset, it still fails to completely close the performance gap between original and rephrasing validation splits.
FENAS: Flexible and Expressive Neural Architecture Search
Authors: Ramakanth Pasunuru, Mohit Bansal
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: image-classification, Image Classification, Language Modeling, Language Modelling, Neural Architecture Search, text-classification, Text Classification

Architecture search is the automatic process of designing the model or cell structure that is optimal for the given dataset or task. Recently, this approach has shown good improvements in terms of performance (tested on language modeling and image classification) with reasonable training speed using a weight sharing-based approach called Efficient Neural Architecture Search (ENAS). In this work, we propose a novel architecture search algorithm called Flexible and Expressible Neural Architecture Search (FENAS), with more flexible and expressible search space than ENAS, in terms of more activation functions, input edges, and atomic operations. Also, our FENAS approach is able to reproduce the well-known LSTM and GRU architectures (unlike ENAS), and is also able to initialize with them for finding architectures more efficiently. We explore this extended search space via evolutionary search and show that FENAS performs significantly better on several popular text classification tasks and performs similar to ENAS on standard language model benchmark. Further, we present ablations and analyses on our FENAS approach.
Enhancing Content Planning for Table-to-Text Generation with Data Understanding and Verification
Authors: Heng Gong, Wei Bi, Xiaocheng Feng, Bing Qin, Xiaojiang Liu, Ting Liu
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Table-to-Text Generation, Text Generation

Neural table-to-text models, which select and order salient data, as well as verbalizing them fluently via surface realization, have achieved promising progress. Based on results from previous work, the performance bottleneck of current models lies in the stage of content planing (selecting and ordering salient content from the input). That is, performance drops drastically when an oracle content plan is replaced by a model-inferred one during surface realization. In this paper, we propose to enhance neural content planning by (1) understanding data values with contextual numerical value representations that bring the sense of value comparison into content planning; (2) verifying the importance and ordering of the selected sequence of records with policy gradient. We evaluated our model on ROTOWIRE and MLB, two datasets on this task, and results show that our model outperforms existing systems with respect to content planning metrics.
Cross-Lingual Dependency Parsing by POS-Guided Word Reordering
Authors: Lu Liu, Yi Zhou, Jianhan Xu, Xiaoqing Zheng, Kai-Wei Chang, Xuanjing Huang
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Dependency Parsing, Language Modeling, Language Modelling, POS, Sentence

We propose a novel approach to cross-lingual dependency parsing based on word reordering. The words in each sentence of a source language corpus are rearranged to meet the word order in a target language under the guidance of a part-of-speech based language model (LM). To obtain the highest reordering score under the LM, a population-based optimization algorithm and its genetic operators are designed to deal with the combinatorial nature of such word reordering. A parser trained on the reordered corpus then can be used to parse sentences in the target language. We demonstrate through extensive experimentation that our approach achieves better or comparable results across 25 target languages (1.73{\%} increase in average), and outperforms a baseline by a significant margin on the languages that are greatly different from the source one. For example, when transferring the English parser to Hindi and Latin, our approach outperforms the baseline by 15.3{\%} and 6.7{\%} respectively.
An Instance Level Approach for Shallow Semantic Parsing in Scientific Procedural Text
Authors: Daivik Swarup, Ahsaas Bajaj, Sheshera Mysore, Tim O{'}Gorman, Rajarshi Das, Andrew McCallum
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Relation Prediction, Semantic Parsing, Sentence

In specific domains, such as procedural scientific text, human labeled data for shallow semantic parsing is especially limited and expensive to create. Fortunately, such specific domains often use rather formulaic writing, such that the different ways of expressing relations in a small number of grammatically similar labeled sentences may provide high coverage of semantic structures in the corpus, through an appropriately rich similarity metric. In light of this opportunity, this paper explores an instance-based approach to the relation prediction sub-task within shallow semantic parsing, in which semantic labels from structurally similar sentences in the training set are copied to test sentences. Candidate similar sentences are retrieved using SciBERT embeddings. For labels where it is possible to copy from a similar sentence we employ an instance level copy network, when this is not possible, a globally shared parametric model is employed. Experiments show our approach outperforms both baseline and prior methods by 0.75 to 3 F1 absolute in the Wet Lab Protocol Corpus and 1 F1 absolute in the Materials Science Procedural Text Corpus.
Effective Crowd-Annotation of Participants, Interventions, and Outcomes in the Text of Clinical Trial Reports
Authors: Markus Zlabinger, Marta Sabou, Sebastian Hofst{\"a}tter, Allan Hanbury
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Sentence, text similarity

The search for Participants, Interventions, and Outcomes (PIO) in clinical trial reports is a critical task in Evidence Based Medicine. For an automatic PIO extraction, high-quality corpora are needed. Obtaining such a corpus from crowdworkers, however, has been shown to be ineffective since (i) workers usually lack domain-specific expertise to conduct the task with sufficient quality, and (ii) the standard approach of annotating entire abstracts of trial reports as one task-instance (i.e. HIT) leads to an uneven distribution in task effort. In this paper, we switch from entire abstract to sentence annotation, referred to as the SenBase approach. We build upon SenBase in SenSupport, where we compensate the lack of domain-specific expertise of crowdworkers by showing for each task-instance similar sentences that are already annotated by experts. Such tailored task-instance examples are retrieved via unsupervised semantic short-text similarity (SSTS) method {--} and we evaluate nine methods to find an effective solution for SenSupport. We compute the Cohen{'}s Kappa agreement between crowd-annotations and gold standard annotations and show that (i) both sentence-based approaches outperform a Baseline approach where entire abstracts are annotated; (ii) supporting annotators with tailored task-instance examples is the best performing approach with Kappa agreements of 0.78/0.75/0.69 for P, I, and O respectively.
Neutralizing Gender Bias in Word Embeddings with Latent Disentanglement and Counterfactual Generation
Authors: Seungjae Shin, Kyungwoo Song, JoonHo Jang, Hyemi Kim, Weonyoung Joo, Il-Chul Moon
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: counterfactual, Disentanglement, Word Embeddings

Recent research demonstrates that word embeddings, trained on the human-generated corpus, have strong gender biases in embedding spaces, and these biases can result in the discriminative results from the various downstream tasks. Whereas the previous methods project word embeddings into a linear subspace for debiasing, we introduce a Latent Disentanglement method with a siamese auto-encoder structure with an adapted gradient reversal layer. Our structure enables the separation of the semantic latent information and gender latent information of given word into the disjoint latent dimensions. Afterwards, we introduce a Counterfactual Generation to convert the gender information of words, so the original and the modified embeddings can produce a gender-neutralized word embedding after geometric alignment regularization, without loss of semantic information. From the various quantitative and qualitative debiasing experiments, our method shows to be better than existing debiasing methods in debiasing word embeddings. In addition, Our method shows the ability to preserve semantic information during debiasing by minimizing the semantic information losses for extrinsic NLP downstream tasks.
Towards Domain-Independent Text Structuring Trainable on Large Discourse Treebanks
Authors: Grigorii Guz, Giuseppe Carenini
Date: 2020-11-01 00:00:00
Proceeding: DT4TP 2020 12

Text structuring is a fundamental step in NLG, especially when generating multi-sentential text. With the goal of fostering more general and data-driven approaches to text structuring, we propose the new and domain-independent NLG task of structuring and ordering a (possibly large) set of EDUs. We then present a solution for this task that combines neural dependency tree induction with pointer networks, and can be trained on large discourse treebanks that have only recently become available. Further, we propose a new evaluation metric that is arguably more suitable for our new task compared to existing content ordering metrics. Finally, we empirically show that our approach outperforms competitive alternatives on the proposed measure and is equivalent in performance with respect to previously established measures.
An Evaluation Method for Diachronic Word Sense Induction
Authors: Ashjan Alsulaimani, Erwan Moreau, Carl Vogel
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Word Sense Induction

The task of Diachronic Word Sense Induction (DWSI) aims to identify the meaning of words from their context, taking the temporal dimension into account. In this paper we propose an evaluation method based on large-scale time-stamped annotated biomedical data, and a range of evaluation measures suited to the task. The approach is applied to two recent DWSI systems, thus demonstrating its relevance and providing an in-depth analysis of the models.
Integrating Task Specific Information into Pretrained Language Models for Low Resource Fine Tuning
Authors: Rui Wang, Shijing Si, Guoyin Wang, Lei Zhang, Lawrence Carin, Ricardo Henao
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Natural Language Understanding

Pretrained Language Models (PLMs) have improved the performance of natural language understanding in recent years. Such models are pretrained on large corpora, which encode the general prior knowledge of natural languages but are agnostic to information characteristic of downstream tasks. This often results in overfitting when fine-tuned with low resource datasets where task-specific information is limited. In this paper, we integrate label information as a task-specific prior into the self-attention component of pretrained BERT models. Experiments on several benchmarks and real-word datasets suggest that the proposed approach can largely improve the performance of pretrained models when fine-tuning with small datasets.
Towards Zero-Shot Conditional Summarization with Adaptive Multi-Task Fine-Tuning
Authors: Travis Goodwin, Max Savery, Dina Demner-Fushman
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Question Answering

Automatic summarization research has traditionally focused on providing high quality general-purpose summaries of documents. However, there are many applications which require more specific summaries, such as supporting question answering or topic-based literature discovery. In this paper we study the problem of conditional summarization in which content selection and surface realization are explicitly conditioned on an ad-hoc natural language question or topic description. Because of the difficulty in obtaining sufficient reference summaries to support arbitrary conditional summarization, we explore the use of multi-task fine-tuning (MTFT) on twenty-one natural language tasks to enable zero-shot conditional summarization on five tasks. We present four new summarization datasets, two novel {``}online{''} or adaptive task-mixing strategies, and report zero-shot performance using T5 and BART, demonstrating that MTFT can improve zero-shot summarization quality.
Regularization of Distinct Strategies for Unsupervised Question Generation
Authors: Junmo Kang, Giwon Hong, Haritz Puerto San Roman, Sung-Hyon Myaeng
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Few-Shot Learning, Question Answering, Question Generation, Question-Generation

Unsupervised question answering (UQA) has been proposed to avoid the high cost of creating high-quality datasets for QA. One approach to UQA is to train a QA model with questions generated automatically. However, the generated questions are either too similar to a word sequence in the context or too drifted from the semantics of the context, thereby making it difficult to train a robust QA model. We propose a novel regularization method based on teacher-student architecture to avoid bias toward a particular question generation strategy and modulate the process of generating individual words when a question is generated. Our experiments demonstrate that we have achieved the goal of generating higher-quality questions for UQA across diverse QA datasets and tasks. We also show that this method can be useful for creating a QA model with few-shot learning.
A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events
Authors: Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Xiangnan Kong, Elke Rundensteiner
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Sentence, Sentence Classification

An adverse drug event (ADE) is an injury resulting from medical intervention related to a drug. Automatic ADE detection from text is either fine-grained (ADE entity recognition) or coarse-grained (ADE assertive sentence classification), with limited efforts leveraging inter-dependencies among the two granularities. We instead propose a multi-grained joint deep network to concurrently learn the ADE entity recognition and ADE sentence classification tasks. Our joint approach takes advantage of their symbiotic relationship, with a transfer of knowledge between the two levels of granularity. Our dual-attention mechanism constructs multiple distinct representations of a sentence that capture both task-specific and semantic information in the sentence, providing stronger emphasis on the key elements essential for sentence classification. Our model improves state-of- art F1-score for both tasks: (i) entity recognition of ADE words (12.5{\%} increase) and (ii) ADE sentence classification (13.6{\%} increase) on MADE 1.0 benchmark of EHR notes.
A Semantics-based Approach to Disclosure Classification in User-Generated Online Content
Authors: Chandan Akiti, Anna Squicciarini, Sarah Rajtmajer
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Semantic Role Labeling, text-classification, Text Classification

As users engage in public discourse, the rate of voluntarily disclosed personal information has seen a steep increase. So-called self-disclosure can result in a number of privacy concerns. Users are often unaware of the sheer amount of personal information they share across online forums, commentaries, and social networks, as well as the power of modern AI to synthesize and gain insights from this data. This paper presents an approach to detect emotional and informational self-disclosure in natural language. We hypothesize that identifying frame semantics can meaningfully support this task. Specifically, we use Semantic Role Labeling to identify the lexical units and their semantic roles that signal self-disclosure. Experimental results on Reddit data show the performance gain of our method when compared to standard text classification methods based on BiLSTM, and BERT. In addition to improved performance, our approach provides insights into the drivers of disclosure behaviors.
Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling
Authors: Dejiao Zhang, Ramesh Nallapati, Henghui Zhu, Feng Nan, Cicero Nogueira dos santos, Kathleen McKeown, Bing Xiang
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Cross-Lingual Document Classification, Document Classification, Domain Adaptation, NER, Unsupervised Domain Adaptation

Unsupervised domain adaptation addresses the problem of leveraging labeled data in a source domain to learn a well-performing model in a target domain where labels are unavailable. In this paper, we improve upon a recent theoretical work (Zhang et al., 2019b) and adopt the Margin Disparity Discrepancy (MDD) unsupervised domain adaptation algorithm to solve the cross-lingual text labeling problems. Experiments on cross-lingual document classification and NER demonstrate the proposed domain adaptation approach advances the state-of-the-art results by a large margin. Specifically, we improve MDD by efficiently optimizing the margin loss on the source domain via Virtual Adversarial Training (VAT). This bridges the gap between theory and the loss function used in the original work Zhang et al.(2019b), and thereby significantly boosts the performance. Our numerical results also indicate that VAT can remarkably improve the generalization performance of both domains for various domain adaptation approaches.