pwc-archive/papers-with-abstracts huggingface dataset:
A Mutual Information Maximization Approach for the Spurious Solution Problem in Weakly Supervised Question Answering
Authors: Zhihong Shao, Lifeng Shang, Qun Liu, Minlie Huang
Date: 2021-06-14 00:00:00
Proceeding: ACL 2021 5
Tasks: Question Answering

Weakly supervised question answering usually has only the final answers as supervision signals while the correct solutions to derive the answers are not provided. This setting gives rise to the spurious solution problem: there may exist many spurious solutions that coincidentally derive the correct answer, but training on such solutions can hurt model performance (e.g., producing wrong solutions or answers). For example, for discrete reasoning tasks as on DROP, there may exist many equations to derive a numeric answer, and typically only one of them is correct. Previous learning methods mostly filter out spurious solutions with heuristics or using model confidence, but do not explicitly exploit the semantic correlations between a question and its solution. In this paper, to alleviate the spurious solution problem, we propose to explicitly exploit such semantic correlations by maximizing the mutual information between question-answer pairs and predicted solutions. Extensive experiments on four question answering datasets show that our method significantly outperforms previous learning methods in terms of task performance and is more effective in training models to produce correct solutions.
End-to-end Neural Diarization: From Transformer to Conformer
Authors: Yi Chieh Liu, Eunjung Han, Chul Lee, Andreas Stolcke
Date: 2021-06-14 00:00:00
Tasks: Data Augmentation

We propose a new end-to-end neural diarization (EEND) system that is based on Conformer, a recently proposed neural architecture that combines convolutional mappings and Transformer to model both local and global dependencies in speech. We first show that data augmentation and convolutional subsampling layers enhance the original self-attentive EEND in the Transformer-based EEND, and then Conformer gives an additional gain over the Transformer-based EEND. However, we notice that the Conformer-based EEND does not generalize as well from simulated to real conversation data as the Transformer-based model. This leads us to quantify the mismatch between simulated data and real speaker behavior in terms of temporal statistics reflecting turn-taking between speakers, and investigate its correlation with diarization error. By mixing simulated and real data in EEND training, we mitigate the mismatch further, with Conformer-based EEND achieving 24% error reduction over the baseline SA-EEND system, and 10% improvement over the best augmented Transformer-based system, on two-speaker CALLHOME data.
Why Can You Lay Off Heads? Investigating How BERT Heads Transfer
Authors: Ting-Rui Chiang, Yun-Nung Chen
Date: 2021-06-14 00:00:00
Tasks: Transfer Learning

The huge size of the widely used BERT family models has led to recent efforts about model distillation. The main goal of distillation is to create a task-agnostic pre-trained model that can be fine-tuned on downstream tasks without fine-tuning its full-sized version. Despite the progress of distillation, to what degree and for what reason a task-agnostic model can be created from distillation has not been well studied. Also, the mechanisms behind transfer learning of those BERT models are not well investigated either. Therefore, this work focuses on analyzing the acceptable deduction when distillation for guiding the future distillation procedure. Specifically, we first inspect the prunability of the Transformer heads in RoBERTa and ALBERT using their head importance estimation proposed by Michel et al. (2019), and then check the coherence of the important heads between the pre-trained task and downstream tasks. Hence, the acceptable deduction of performance on the pre-trained task when distilling a model can be derived from the results, and we further compare the behavior of the pruned model before and after fine-tuning. Our studies provide guidance for future directions about BERT family model distillation.
Toward Diverse Precondition Generation
Authors: Heeyoung Kwon, Nathanael Chambers, Niranjan Balasubramanian
Date: 2021-06-14 00:00:00
Proceeding: Joint Conference on Lexical and Computational Semantics 2021
Tasks: Diversity

Language understanding must identify the logical connections between events in a discourse, but core events are often unstated due to their commonsense nature. This paper fills in these missing events by generating precondition events. Precondition generation can be framed as a sequence-to-sequence problem: given a target event, generate a possible precondition. However, in most real-world scenarios, an event can have several preconditions, requiring diverse generation -- a challenge for standard seq2seq approaches. We propose DiP, a Diverse Precondition generation system that can generate unique and diverse preconditions. DiP uses a generative process with three components -- an event sampler, a candidate generator, and a post-processor. The event sampler provides control codes (precondition triggers) which the candidate generator uses to focus its generation. Unlike other conditional generation systems, DiP automatically generates control codes without training on diverse examples. Analysis against baselines reveals that DiP improves the diversity of preconditions significantly while also generating more preconditions.
Cross-utterance Reranking Models with BERT and Graph Convolutional Networks for Conversational Speech Recognition
Authors: Shih-Hsuan Chiu, Tien-Hong Lo, Fu-An Chao, Berlin Chen
Date: 2021-06-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modelling, Reranking, speech-recognition, Speech Recognition, Text Summarization

How to effectively incorporate cross-utterance information cues into a neural language model (LM) has emerged as one of the intriguing issues for automatic speech recognition (ASR). Existing research efforts on improving contextualization of an LM typically regard previous utterances as a sequence of additional input and may fail to capture complex global structural dependencies among these utterances. In view of this, we in this paper seek to represent the historical context information of an utterance as graph-structured data so as to distill cross-utterances, global word interaction relationships. To this end, we apply a graph convolutional network (GCN) on the resulting graph to obtain the corresponding GCN embeddings of historical words. GCN has recently found its versatile applications on social-network analysis, text summarization, and among others due mainly to its ability of effectively capturing rich relational information among elements. However, GCN remains largely underexplored in the context of ASR, especially for dealing with conversational speech. In addition, we frame ASR N-best reranking as a prediction problem, leveraging bidirectional encoder representations from transformers (BERT) as the vehicle to not only seize the local intrinsic word regularity patterns inherent in a candidate hypothesis but also incorporate the cross-utterance, historical word interaction cues distilled by GCN for promoting performance. Extensive experiments conducted on the AMI benchmark dataset seem to confirm the pragmatic utility of our methods, in relation to some current top-of-the-line methods.
Sentiment Analysis of Covid-19 Tweets using Evolutionary Classification-Based LSTM Model
Authors: Arunava Kumar Chakraborty, Sourav Das, Anup Kumar Kolya
Date: 2021-06-13 00:00:00
Tasks: Sentiment Analysis

As the Covid-19 outbreaks rapidly all over the world day by day and also affects the lives of million, a number of countries declared complete lock-down to check its intensity. During this lockdown period, social media plat-forms have played an important role to spread information about this pandemic across the world, as people used to express their feelings through the social networks. Considering this catastrophic situation, we developed an experimental approach to analyze the reactions of people on Twitter taking into ac-count the popular words either directly or indirectly based on this pandemic. This paper represents the sentiment analysis on collected large number of tweets on Coronavirus or Covid-19. At first, we analyze the trend of public sentiment on the topics related to Covid-19 epidemic using an evolutionary classification followed by the n-gram analysis. Then we calculated the sentiment ratings on collected tweet based on their class. Finally, we trained the long-short term network using two types of rated tweets to predict sentiment on Covid-19 data and obtained an overall accuracy of 84.46%.
GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio
Authors: Guoguo Chen, Shuzhou Chai, Guanbo Wang, Jiayu Du, Wei-Qiang Zhang, Chao Weng, Dan Su, Daniel Povey, Jan Trmal, Junbo Zhang, Mingjie Jin, Sanjeev Khudanpur, Shinji Watanabe, Shuaijiang Zhao, Wei Zou, Xiangang Li, Xuchen Yao, Yongqing Wang, Yujun Wang, Zhao You, Zhiyong Yan
Date: 2021-06-13 00:00:00
Tasks: Sentence, speech-recognition, Speech Recognition

This paper introduces GigaSpeech, an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised and unsupervised training. Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science, sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable for speech recognition training, and to filter out segments with low-quality transcription. For system training, GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h. For our 10,000-hour XL training subset, we cap the word error rate at 4% during the filtering/validation stage, and for all our other smaller training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the other hand, are re-processed by professional human transcribers to ensure high transcription quality. Baseline systems are provided for popular speech recognition toolkits, namely Athena, ESPnet, Kaldi and Pika.
InfoBehavior: Self-supervised Representation Learning for Ultra-long Behavior Sequence via Hierarchical Grouping
Authors: Runshi Liu, Pengda Qin, Yuhong Li, Weigao Wen, Dong Li, Kefeng Deng, Qiang Wu
Date: 2021-06-13 00:00:00
Tasks: feature selection, Management, Representation Learning

E-commerce companies have to face abnormal sellers who sell potentially-risky products. Typically, the risk can be identified by jointly considering product content (e.g., title and image) and seller behavior. This work focuses on behavior feature extraction as behavior sequences can provide valuable clues for the risk discovery by reflecting the sellers' operation habits. Traditional feature extraction techniques heavily depend on domain experts and adapt poorly to new tasks. In this paper, we propose a self-supervised method InfoBehavior to automatically extract meaningful representations from ultra-long raw behavior sequences instead of the costly feature selection procedure. InfoBehavior utilizes Bidirectional Transformer as feature encoder due to its excellent capability in modeling long-term dependency. However, it is intractable for commodity GPUs because the time and memory required by Transformer grow quadratically with the increase of sequence length. Thus, we propose a hierarchical grouping strategy to aggregate ultra-long raw behavior sequences to length-processable high-level embedding sequences. Moreover, we introduce two types of pretext tasks. Sequence-related pretext task defines a contrastive-based training objective to correctly select the masked-out coarse-grained/fine-grained behavior sequences against other "distractor" behavior sequences; Domain-related pretext task designs a classification training objective to correctly predict the domain-specific statistical results of anomalous behavior. We show that behavior representations from the pre-trained InfoBehavior can be directly used or integrated with features from other side information to support a wide range of downstream tasks. Experimental results demonstrate that InfoBehavior significantly improves the performance of Product Risk Management and Intellectual Property Protection.
Don't Rule Out Monolingual Speakers: A Method For Crowdsourcing Machine Translation Data
Authors: Rajat Bhatnagar, Ananya Ganesh, Katharina Kann
Date: 2021-06-12 00:00:00
Proceeding: ACL 2021 5
Tasks: Machine Translation, Sentence, Translation

High-performing machine translation (MT) systems can help overcome language barriers while making it possible for everyone to communicate and use language technologies in the language of their choice. However, such systems require large amounts of parallel sentences for training, and translators can be difficult to find and expensive. Here, we present a data collection strategy for MT which, in contrast, is cheap and simple, as it does not require bilingual speakers. Based on the insight that humans pay specific attention to movements, we use graphics interchange formats (GIFs) as a pivot to collect parallel sentences from monolingual annotators. We use our strategy to collect data in Hindi, Tamil and English. As a baseline, we also collect data using images as a pivot. We perform an intrinsic evaluation by manually evaluating a subset of the sentence pairs and an extrinsic evaluation by finetuning mBART on the collected data. We find that sentences collected via GIFs are indeed of higher quality.
Can Transformer Language Models Predict Psychometric Properties?
Authors: Antonio Laverghetta Jr., Animesh Nighojkar, Jamshidbek Mirzakhalov, John Licato
Date: 2021-06-12 00:00:00
Proceeding: Joint Conference on Lexical and Computational Semantics 2021
Tasks: Diagnostic

Transformer-based language models (LMs) continue to advance state-of-the-art performance on NLP benchmark tasks, including tasks designed to mimic human-inspired "commonsense" competencies. To better understand the degree to which LMs can be said to have certain linguistic reasoning skills, researchers are beginning to adapt the tools and concepts of the field of psychometrics. But to what extent can the benefits flow in the other direction? I.e., can LMs be of use in predicting what the psychometric properties of test items will be when those items are given to human participants? We gather responses from numerous human participants and LMs (transformer and non-transformer-based) on a broad diagnostic test of linguistic competencies. We then use the responses to calculate standard psychometric properties of the items in the diagnostic test, using the human responses and the LM responses separately. We then determine how well these two sets of predictions match. We find cases in which transformer-based LMs predict psychometric properties consistently well in certain categories but consistently poorly in others, thus providing new insights into fundamental similarities and differences between human and LM reasoning.
A Pseudo Label-wise Attention Network for Automatic ICD Coding
Authors: Yifan Wu, Min Zeng, Ying Yu, Min Li
Date: 2021-06-12 00:00:00
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Pseudo Label

Automatic International Classification of Diseases (ICD) coding is defined as a kind of text multi-label classification problem, which is difficult because the number of labels is very large and the distribution of labels is unbalanced. The label-wise attention mechanism is widely used in automatic ICD coding because it can assign weights to every word in full Electronic Medical Records (EMR) for different ICD codes. However, the label-wise attention mechanism is computational redundant and costly. In this paper, we propose a pseudo label-wise attention mechanism to tackle the problem. Instead of computing different attention modes for different ICD codes, the pseudo label-wise attention mechanism automatically merges similar ICD codes and computes only one attention mode for the similar ICD codes, which greatly compresses the number of attention modes and improves the predicted accuracy. In addition, we apply a more convenient and effective way to obtain the ICD vectors, and thus our model can predict new ICD codes by calculating the similarities between EMR vectors and ICD vectors. Extensive experiments show the superior performance of our model. On the public MIMIC-III dataset and private Xiangya dataset, our model achieves micro f1 of 0.583 and 0.806, respectively, which outperforms other competing models. Furthermore, we verify the ability of our model in predicting new ICD codes. The case study shows how pseudo label-wise attention works, and demonstrates the effectiveness of pseudo label-wise attention mechanism.
Machine Translation into Low-resource Language Varieties
Authors: Sachin Kumar, Antonios Anastasopoulos, Shuly Wintner, Yulia Tsvetkov
Date: 2021-06-12 00:00:00
Proceeding: ACL 2021 5
Tasks: Machine Translation, Translation

State-of-the-art machine translation (MT) systems are typically trained to generate the "standard" target language; however, many languages have multiple varieties (regional varieties, dialects, sociolects, non-native varieties) that are different from the standard language. Such varieties are often low-resource, and hence do not benefit from contemporary NLP solutions, MT included. We propose a general framework to rapidly adapt MT systems to generate language varieties that are close to, but different from, the standard target language, using no parallel (source--variety) data. This also includes adaptation of MT systems to low-resource typologically-related target languages. We experiment with adapting an English--Russian MT system to generate Ukrainian and Belarusian, an English--Norwegian Bokm{\aa}l system to generate Nynorsk, and an English--Arabic system to generate four Arabic dialects, obtaining significant improvements over competitive baselines.
Exploiting Parallel Corpora to Improve Multilingual Embedding based Document and Sentence Alignment
Authors: Dilan Sachintha, Lakmali Piyarathna, Charith Rajitha, Surangika Ranathunga
Date: 2021-06-12 00:00:00
Tasks: Sentence

Multilingual sentence representations pose a great advantage for low-resource languages that do not have enough data to build monolingual models on their own. These multilingual sentence representations have been separately exploited by few research for document and sentence alignment. However, most of the low-resource languages are under-represented in these pre-trained models. Thus, in the context of low-resource languages, these models have to be fine-tuned for the task at hand, using additional data sources. This paper presents a weighting mechanism that makes use of available small-scale parallel corpora to improve the performance of multilingual sentence representations on document and sentence alignment. Experiments are conducted with respect to two low-resource languages, Sinhala and Tamil. Results on a newly created dataset of Sinhala-English, Tamil-English, and Sinhala-Tamil show that this new weighting mechanism significantly improves both document and sentence alignment. This dataset, as well as the source-code, is publicly released.
Every Bite Is an Experience: Key Point Analysis of Business Reviews
Authors: Roy Bar-Haim, Lilach Eden, Yoav Kantor, Roni Friedman, Noam Slonim
Date: 2021-06-12 00:00:00
Proceeding: ACL 2021 5
Tasks: Sentiment Analysis

Previous work on review summarization focused on measuring the sentiment toward the main aspects of the reviewed product or business, or on creating a textual summary. These approaches provide only a partial view of the data: aspect-based sentiment summaries lack sufficient explanation or justification for the aspect rating, while textual summaries do not quantify the significance of each element, and are not well-suited for representing conflicting views. Recently, Key Point Analysis (KPA) has been proposed as a summarization framework that provides both textual and quantitative summary of the main points in the data. We adapt KPA to review data by introducing Collective Key Point Mining for better key point extraction; integrating sentiment analysis into KPA; identifying good key point candidates for review summaries; and leveraging the massive amount of available reviews and their metadata. We show empirically that these novel extensions of KPA substantially improve its performance. We demonstrate that promising results can be achieved without any domain-specific annotation, while human supervision can lead to further improvement.
Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation
Authors: Yang Feng, Shuhao Gu, Dengji Guo, Zhengxin Yang, Chenze Shao
Date: 2021-06-12 00:00:00
Proceeding: ACL 2021 5
Tasks: Decoder, Knowledge Distillation, L2 Regularization, Machine Translation, Translation

Although teacher forcing has become the main training paradigm for neural machine translation, it usually makes predictions only conditioned on past information, and hence lacks global planning for the future. To address this problem, we introduce another decoder, called seer decoder, into the encoder-decoder framework during training, which involves future information in target predictions. Meanwhile, we force the conventional decoder to simulate the behaviors of the seer decoder via knowledge distillation. In this way, at test the conventional decoder can perform like the seer decoder without the attendance of it. Experiment results on the Chinese-English, English-German and English-Romanian translation tasks show our method can outperform competitive baselines significantly and achieves greater improvements on the bigger data sets. Besides, the experiments also prove knowledge distillation the best way to transfer knowledge from the seer decoder to the conventional decoder compared to adversarial learning and L2 regularization.
A Sentence-level Hierarchical BERT Model for Document Classification with Limited Labelled Data
Authors: Jinghui Lu, Maeve Henchion, Ivan Bacher, Brian Mac Namee
Date: 2021-06-12 00:00:00
Tasks: Classification, Document Classification, Sentence

Training deep learning models with limited labelled data is an attractive scenario for many NLP tasks, including document classification. While with the recent emergence of BERT, deep learning language models can achieve reasonably good performance in document classification with few labelled instances, there is a lack of evidence in the utility of applying BERT-like models on long document classification. This work introduces a long-text-specific model -- the Hierarchical BERT Model (HBM) -- that learns sentence-level features of the text and works well in scenarios with limited labelled data. Various evaluation experiments have demonstrated that HBM can achieve higher performance in document classification than the previous state-of-the-art methods with only 50 to 200 labelled instances, especially when documents are long. Also, as an extra benefit of HBM, the salient sentences identified by learned HBM are useful as explanations for labelling documents based on a user study.