pwc-archive/papers-with-abstracts huggingface dataset:
``Hold on honey, men at work'': A semi-supervised approach to detecting sexism in sitcoms
Authors: Smriti Singh, Tanvi Anand, Arijit Ghosh Chowdhury, Zeerak Waseem
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Classification, Sentence, Sentence Classification, text-classification, Text Classification

Television shows play an important role inpropagating societal norms. Owing to the popularity of the situational comedy (sitcom) genre, it contributes significantly to the over-all development of society. In an effort to analyze the content of television shows belong-ing to this genre, we present a dataset of dialogue turns from popular sitcoms annotated for the presence of sexist remarks. We train a text classification model to detect sexism using domain adaptive learning. We apply the model to our dataset to analyze the evolution of sexist content over the years. We propose a domain-specific semi-supervised architecture for the aforementioned detection of sexism.Through extensive experiments, we show that our model often yields better classification performance over generic deep learn-ing based sentence classification that does not employ domain-specific training. We find that while sexism decreases over time on average,the proportion of sexist dialogue for the most sexist sitcom actually increases. A quantitative analysis along with a detailed error analysis presents the case for our proposed methodology
Observing the Learning Curve of NMT Systems With Regard to Linguistic Phenomena
Authors: Patrick Stadler, Vivien Macketanz, Eleftherios Avramidis
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Machine Translation, NMT, Translation

In this paper we present our observations and evaluations by observing the linguistic performance of the system on several steps on the training process of various English-to-German Neural Machine Translation models. The linguistic performance is measured through a semi-automatic process using a test suite. Among several linguistic observations, we find that the translation quality of some linguistic categories decreased within the recorded iterations. Additionally, we notice some drops of the translation quality of certain categories when using a larger corpus.
Tools Impact on the Quality of Annotations for Chat Untangling
Authors: Jhonny Cerezo, Felipe Bravo-Marquez, Alexandre Henri Bergel
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5

The quality of the annotated data directly influences in the success of supervised NLP models. However, creating annotated datasets is often time-consuming and expensive. Although the annotation tool takes an important role, we know little about how it influences annotation quality. We compare the quality of annotations for the task of chat-untangling made by non-experts annotators using two different tools. The first is SLATE, an existing command-line based tool, and the second is Parlay, a new tool we developed that integrates mouse interaction and visual links. Our experimental results indicate that, while both tools perform similarly in terms of annotation quality, Parlay offers a significantly better user experience.
How Many Layers and Why? An Analysis of the Model Depth in Transformers
Authors: Antoine Simoulin, Benoit Crabb{\'e}
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Specificity

In this study, we investigate the role of the multiple layers in deep transformer models. We design a variant of Albert that dynamically adapts the number of layers for each token of the input. The key specificity of Albert is that weights are tied across layers. Therefore, the stack of encoder layers iteratively repeats the application of the same transformation function on the input. We interpret the repetition of this application as an iterative process where the token contextualized representations are progressively refined. We analyze this process at the token level during pre-training, fine-tuning, and inference. We show that tokens do not require the same amount of iterations and that difficult or crucial tokens for the task are subject to more iterations.
Edit Distance Based Curriculum Learning for Paraphrase Generation
Authors: Sora Kadotani, Tomoyuki Kajiwara, Yuki Arase, Makoto Onizuka
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Machine Translation, Paraphrase Generation, Translation

Curriculum learning has improved the quality of neural machine translation, where only source-side features are considered in the metrics to determine the difficulty of translation. In this study, we apply curriculum learning to paraphrase generation for the first time. Different from machine translation, paraphrase generation allows a certain level of discrepancy in semantics between source and target, which results in diverse transformations from lexical substitution to reordering of clauses. Hence, the difficulty of transformations requires considering both source and target contexts. Experiments on formality transfer using GYAFC showed that our curriculum learning with edit distance improves the quality of paraphrase generation. Additionally, the proposed method improves the quality of difficult samples, which was not possible for previous methods.
Changing the Basis of Contextual Representations with Explicit Semantics
Authors: Tam{\'a}s Ficsor, G{\'a}bor Berend
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5

The application of transformer-based contextual representations has became a de facto solution for solving complex NLP tasks. Despite their successes, such representations are arguably opaque as their latent dimensions are not directly interpretable. To alleviate this limitation of contextual representations, we devise such an algorithm where the output representation expresses human-interpretable information of each dimension. We achieve this by constructing a transformation matrix based on the semantic content of the embedding space and predefined semantic categories using Hellinger distance. We evaluate our inferred representations on supersense prediction task. Our experiments reveal that the interpretable nature of transformed contextual representations makes it possible to accurately predict the supersense category of a word by simply looking for its transformed coordinate with the largest coefficient. We quantify the effects of our proposed transformation when applied over traditional dense contextual embeddings. We additionally investigate and report consistent improvements for the integration of sparse contextual word representations into our proposed algorithm.
Personal Bias in Prediction of Emotions Elicited by Textual Opinions
Authors: Piotr Milkowski, Marcin Gruza, Kamil Kanclerz, Przemyslaw Kazienko, Damian Grimling, Jan Kocon
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Articles, Recommendation Systems

Analysis of emotions elicited by opinions, comments, or articles commonly exploits annotated corpora, in which the labels assigned to documents average the views of all annotators, or represent a majority decision. The models trained on such data are effective at identifying the general views of the population. However, their usefulness for predicting the emotions evoked by the textual content in a particular individual is limited. In this paper, we present a study performed on a dataset containing 7,000 opinions, each annotated by about 50 people with two dimensions: valence, arousal, and with intensity of eight emotions from Plutchik{'}s model. Our study showed that individual responses often significantly differed from the mean. Therefore, we proposed a novel measure to estimate this effect {--} Personal Emotional Bias (PEB). We also developed a new BERT-based transformer architecture to predict emotions from an individual human perspective. We found PEB a major factor for improving the quality of personalized reasoning. Both the method and measure may boost the quality of content recommendation systems and personalized solutions that protect users from hate speech or unwanted content, which are highly subjective in nature.
MVP-BERT: Multi-Vocab Pre-training for Chinese BERT
Authors: Wei Zhu
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Chinese Word Segmentation, Language Modeling, Language Modelling

Despite the development of pre-trained language models (PLMs) significantly raise the performances of various Chinese natural language processing (NLP) tasks, the vocabulary (vocab) for these Chinese PLMs remains to be the one provided by Google Chinese BERT (CITATION), which is based on Chinese characters (chars). Second, the masked language model pre-training is based on a single vocab, limiting its downstream task performances. In this work, we first experimentally demonstrate that building a vocab via Chinese word segmentation (CWS) guided sub-word tokenization (SGT) can improve the performances of Chinese PLMs. Then we propose two versions of multi-vocab pre-training (MVP), Hi-MVP and AL-MVP, to improve the models{'} expressiveness. Experiments show that: (a) MVP training strategies improve PLMs{'} downstream performances, especially it can improve the PLM{'}s performances on span-level tasks; (b) our AL-MVP outperforms the recent AMBERT (CITATION) after large-scale pre-training, and it is more robust against adversarial attacks.
CMTA: COVID-19 Misinformation Multilingual Analysis on Twitter
Authors: Raj Pranesh, Mehrdad Farokhenajd, Ambesh Shekhar, Genoveva Vargas-Solar
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Misinformation, Rumour Detection, Transfer Learning

The internet has actually come to be an essential resource of health knowledge for individuals around the world in the present situation of the coronavirus condition pandemic(COVID-19). During pandemic situations, myths, sensationalism, rumours and misinformation, generated intentionally or unintentionally, spread rapidly through social networks. Twitter is one of these popular social networks people use to share COVID-19 related news, information, and thoughts that reflect their perception and opinion about the pandemic. Evaluation of tweets for recognizing misinformation can create beneficial understanding to review the top quality and also the readability of online information concerning the COVID-19. This paper presents a multilingual COVID-19 related tweet analysis method, CMTA, that uses BERT, a deep learning model for multilingual tweet misinformation detection and classification. CMTA extracts features from multilingual textual data, which is then categorized into specific information classes. Classification is done by a Dense-CNN model trained on tweets manually annotated into information classes (i.e., {`}false{'}, {`}partly false{'}, {`}misleading{'}). The paper presents an analysis of multilingual tweets from February to June, showing the distribution type of information spread across different languages. To access the performance of the CMTA multilingual model, we performed a comparative analysis of 8 monolingual model and CMTA for the misinformation detection task. The results show that our proposed CMTA model has surpassed various monolingual models which consolidated the fact that through transfer learning a multilingual framework could be developed.
Predicting pragmatic discourse features in the language of adults with autism spectrum disorder
Authors: Christine Yang, Duanchen Liu, Qingyun Yang, Zoey Liu, Emily Prud{'}hommeaux
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Informativeness

Individuals with autism spectrum disorder (ASD) experience difficulties in social aspects of communication, but the linguistic characteristics associated with deficits in discourse and pragmatic expression are often difficult to precisely identify and quantify. We are currently collecting a corpus of transcribed natural conversations produced in an experimental setting in which participants with and without ASD complete a number of collaborative tasks with their neurotypical peers. Using this dyadic conversational data, we investigate three pragmatic features {--} politeness, uncertainty, and informativeness {--} and present a dataset of utterances annotated for each of these features on a three-point scale. We then introduce ongoing work in developing and training neural models to automatically predict these features, with the goal of identifying the same between-groups differences that are observed using manual annotations. We find the best performing model for all three features is a feed-forward neural network trained with BERT embeddings. Our models yield higher accuracy than ones used in previous approaches for deriving these features, with F1 exceeding 0.82 for all three pragmatic features.
SumPubMed: Summarization Dataset of PubMed Scientific Articles
Authors: Vivek Gupta, Prerna Bharti, Pegah Nokhiz, Harish Karnick
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Articles, Informativeness, Text Summarization

Most earlier work on text summarization is carried out on news article datasets. The summary in these datasets is naturally located at the beginning of the text. Hence, a model can spuriously utilize this correlation for summary generation instead of truly learning to summarize. To address this issue, we constructed a new dataset, SumPubMed , using scientific articles from the PubMed archive. We conducted a human analysis of summary coverage, redundancy, readability, coherence, and informativeness on SumPubMed . SumPubMed is challenging because (a) the summary is distributed throughout the text (not-localized on top), and (b) it contains rare domain-specific scientific terms. We observe that seq2seq models that adequately summarize news articles struggle to summarize SumPubMed . Thus, SumPubMed opens new avenues for the future improvement of models as well as the development of new evaluation metrics.
A Case Study of Analysis of Construals in Language on Social Media Surrounding a Crisis Event
Authors: Lolo Aboufoul, Khyati Mahajan, Tiffany Gallicano, Sara Levens, Samira Shaikh
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Topic Models

The events that took place at the Unite the Right rally held in Charlottesville, Virginia on August 11-12, 2017 caused intense reaction on social media from users across the political spectrum. We present a novel application of psycholinguistics - specifically, construal level theory - to analyze the language on social media around this event of social import through topic models. We find that including psycholinguistic measures of concreteness as covariates in topic models can lead to informed analysis of the language surrounding an event of political import.
Cross-lingual Evidence Improves Monolingual Fake News Detection
Authors: Daryna Dementieva, Alexander Panchenko
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Fake News Detection, News Classification

Misleading information spreads on the Internet at an incredible speed, which can lead to irreparable consequences in some cases. Therefore, it is becoming essential to develop fake news detection technologies. While substantial work has been done in this direction, one of the limitations of the current approaches is that these models are focused only on one language and do not use multilingual information. In this work, we propose a new technique based on cross-lingual evidence (CE) that can be used for fake news detection and improve existing approaches. The hypothesis of the usage of cross-lingual evidence as a feature for fake news detection is confirmed, firstly, by manual experiment based on a set of known true and fake news. Besides, we compared our fake news classification system based on the proposed feature with several strong baselines on two multi-domain datasets of general-topic news and one newly fake COVID-19 news dataset showing that combining cross-lingual evidence with strong baselines such as RoBERTa yields significant improvements in fake news detection.
Neural Machine Translation with Synchronous Latent Phrase Structure
Authors: Shintaro Harada, Taro Watanabe
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Constituency Parsing, Decoder, Machine Translation, Translation

It is reported that grammatical information is useful for machine translation (MT) task. However, the annotation of grammatical information requires the highly human resources. Furthermore, it is not trivial to adapt grammatical information to MT since grammatical annotation usually adapts tokenization standards which might not be suitable to capture the relation of two languages, and the use of sub-word tokenization, e.g., Byte-Pair-Encoding, to alleviate out-of-vocabulary problem might not be compatible with those annotations. In this work, we propose two methods to explicitly incorporate grammatical information without supervising annotation; first, latent phrase structure is induced in an unsupervised fashion from a multi-head attention mechanism; second, the induced phrase structures in encoder and decoder are synchronized so that they are compatible with each other using constraints during training. We demonstrate that our approach produces better performance and explainability in two tasks, translation and alignment tasks without extra resources. Although we could not obtain the high quality phrase structure in constituency parsing when evaluated monolingually, we find that the induced phrase structures enhance the explainability of translation through the synchronization constraint.
Zero Pronouns Identification based on Span prediction
Authors: Sei Iwata, Taro Watanabe, Masaaki Nagata
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Prediction

The presence of zero-pronoun (ZP) greatly affects the downstream tasks of NLP in pro-drop languages such as Japanese and Chinese. To tackle the problem, the previous works identified ZPs as sequence labeling on the word sequence or the linearlized tree nodes of the input. We propose a novel approach to ZP identification by casting it as a query-based argument span prediction task. Given a predicate as a query, our model predicts the omission with ZP. In the experiments, our model surpassed the sequence labeling baseline.
On the differences between BERT and MT encoder spaces and how to address them in translation tasks
Authors: Ra{\'u}l V{\'a}zquez, Hande Celikkanat, Mathias Creutz, J{\"o}rg Tiedemann
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Machine Translation, NMT, Translation

Various studies show that pretrained language models such as BERT cannot straightforwardly replace encoders in neural machine translation despite their enormous success in other tasks. This is even more astonishing considering the similarities between the architectures. This paper sheds some light on the embedding spaces they create, using average cosine similarity, contextuality metrics and measures for representational similarity for comparison, revealing that BERT and NMT encoder representations look significantly different from one another. In order to address this issue, we propose a supervised transformation from one into the other using explicit alignment and fine-tuning. Our results demonstrate the need for such a transformation to improve the applicability of BERT in MT.
Synchronous Syntactic Attention for Transformer Neural Machine Translation
Authors: Hiroyuki Deguchi, Akihiro Tamura, Takashi Ninomiya
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Decoder, Machine Translation, Translation

This paper proposes a novel attention mechanism for Transformer Neural Machine Translation, {``}Synchronous Syntactic Attention,{''} inspired by synchronous dependency grammars. The mechanism synchronizes source-side and target-side syntactic self-attentions by minimizing the difference between target-side self-attentions and the source-side self-attentions mapped by the encoder-decoder attention matrix. The experiments show that the proposed method improves the translation performance on WMT14 En-De, WMT16 En-Ro, and ASPEC Ja-En (up to +0.38 points in BLEU).
Enhancing Entity Boundary Detection for Better Chinese Named Entity Recognition
Authors: Chun Chen, Fang Kong
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Boundary Detection, Chinese Named Entity Recognition, Graph Attention, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

In comparison with English, due to the lack of explicit word boundary and tenses information, Chinese Named Entity Recognition (NER) is much more challenging. In this paper, we propose a boundary enhanced approach for better Chinese NER. In particular, our approach enhances the boundary information from two perspectives. On one hand, we enhance the representation of the internal dependency of phrases by an additional Graph Attention Network(GAT) layer. On the other hand, taking the entity head-tail prediction (i.e., boundaries) as an auxiliary task, we propose an unified framework to learn the boundary information and recognize the NE jointly. Experiments on both the OntoNotes and the Weibo corpora show the effectiveness of our approach.
Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer
Authors: Sharmila Reddy Nangi, Niyati Chhaya, Sopan Khosla, Nikhil Kaushik, Harshit Nyati
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Attribute, counterfactual, Disentanglement, Sentence, Style Transfer, Text Style Transfer, Unsupervised Text Style Transfer

Disentanglement of latent representations into content and style spaces has been a commonly employed method for unsupervised text style transfer. These techniques aim to learn the disentangled representations and tweak them to modify the style of a sentence. In this paper, we propose a counterfactual-based method to modify the latent representation, by posing a {`}what-if{'} scenario. This simple and disciplined approach also enables a fine-grained control on the transfer strength. We conduct experiments with the proposed methodology on multiple attribute transfer tasks like Sentiment, Formality and Excitement to support our hypothesis.
Video Paragraph Captioning as a Text Summarization Task
Authors: Hui Liu, Xiaojun Wan
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Sentence, Text Summarization

Video paragraph captioning aims to generate a set of coherent sentences to describe a video that contains several events. Most previous methods simplify this task by using ground-truth event segments. In this work, we propose a novel framework by taking this task as a text summarization task. We first generate lots of sentence-level captions focusing on different video clips and then summarize these captions to obtain the final paragraph caption. Our method does not depend on ground-truth event segments. Experiments on two popular datasets ActivityNet Captions and YouCookII demonstrate the advantages of our new framework. On the ActivityNet dataset, our method even outperforms some previous methods using ground-truth event segment labels.
How Helpful is Inverse Reinforcement Learning for Table-to-Text Generation?
Authors: Sayan Ghosh, Zheng Qi, Snigdha Chaturvedi, Shashank Srivastava
Date: 2021-08-01 00:00:00
Proceeding: ACL 2021 5
Tasks: Domain Adaptation, Hallucination, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Table-to-Text Generation, Text Generation

Existing approaches for the Table-to-Text task suffer from issues such as missing information, hallucination and repetition. Many approaches to this problem use Reinforcement Learning (RL), which maximizes a single manually defined reward, such as BLEU. In this work, we instead pose the Table-to-Text task as Inverse Reinforcement Learning (IRL) problem. We explore using multiple interpretable unsupervised reward components that are combined linearly to form a composite reward function. The composite reward function and the description generator are learned jointly. We find that IRL outperforms strong RL baselines marginally. We further study the generalization of learned IRL rewards in scenarios involving domain adaptation. Our experiments reveal significant challenges in using IRL for this task.