pwc-archive/papers-with-abstracts huggingface dataset:
It is AI’s Turn to Ask Humans a Question: Question-Answer Pair Generation for Children’s Story Books
Authors: Bingsheng Yao, Dakuo Wang, Tongshuang Wu, Zheng Zhang, Toby Li, Mo Yu, Ying Xu
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Answer Generation, Question-Answer-Generation, Question Answering

Existing question answering (QA) techniques are created mainly to answer questions asked by humans. But in educational applications, teachers often need to decide what questions they should ask, in order to help students to improve their narrative understanding capabilities. We design an automated question-answer generation (QAG) system for this education scenario: given a story book at the kindergarten to eighth-grade level as input, our system can automatically generate QA pairs that are capable of testing a variety of dimensions of a student’s comprehension skills. Our proposed QAG model architecture is demonstrated using a new expert-annotated FairytaleQA dataset, which has 278 child-friendly storybooks with 10,580 QA pairs. Automatic and human evaluations show that our model outperforms state-of-the-art QAG baseline systems. On top of our QAG system, we also start to build an interactive story-telling application for the future real-world deployment in this educational scenario.
Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning
Authors: Rongzhi Zhang, Yue Yu, Pranav Shetty, Le Song, Chao Zhang
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Weakly-supervised Learning

Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult. We study interactive weakly-supervised learning—the problem of iteratively and automatically discovering novel labeling rules from data to improve the WSL model. Our proposed model, named PRBoost, achieves this goal via iterative prompt-based rule discovery and model boosting. It uses boosting to identify large-error instances and discovers candidate rules from them by prompting pre-trained LMs with rule templates. The candidate rules are judged by human experts, and the accepted rules are used to generate complementary weak labels and strengthen the current model. Experiments on four tasks show PRBoost outperforms state-of-the-art WSL baselines up to 7.1%, and bridges the gaps with fully supervised models.
Constrained Multi-Task Learning for Bridging Resolution
Authors: Hideo Kobayashi, Yufang Hou, Vincent Ng
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Multi-Task Learning

We examine the extent to which supervised bridging resolvers can be improved without employing additional labeled bridging data by proposing a novel constrained multi-task learning framework for bridging resolution, within which we (1) design cross-task consistency constraints to guide the learning process; (2) pre-train the entity coreference model in the multi-task framework on the large amount of publicly available coreference data; and (3) integrating prior knowledge encoded in rule-based resolvers. Our approach achieves state-of-the-art results on three standard evaluation corpora.
De-Bias for Generative Extraction in Unified NER Task
Authors: Shuai Zhang, Yongliang Shen, Zeqi Tan, Yiquan Wu, Weiming Lu
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Attribute, Data Augmentation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentence

Named entity recognition (NER) is a fundamental task to recognize specific types of entities from a given sentence. Depending on how the entities appear in the sentence, it can be divided into three subtasks, namely, Flat NER, Nested NER, and Discontinuous NER. Among the existing approaches, only the generative model can be uniformly adapted to these three subtasks. However, when the generative model is applied to NER, its optimization objective is not consistent with the task, which makes the model vulnerable to the incorrect biases. In this paper, we analyze the incorrect biases in the generation process from a causality perspective and attribute them to two confounders: pre-context confounder and entity-order confounder. Furthermore, we design Intra- and Inter-entity Deconfounding Data Augmentation methods to eliminate the above confounders according to the theory of backdoor adjustment. Experiments show that our method can improve the performance of the generative NER model in various datasets.
Nested Named Entity Recognition with Span-level Graphs
Authors: Juncheng Wan, Dongyu Ru, Weinan Zhang, Yong Yu
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Nested Named Entity Recognition, Retrieval

Span-based methods with the neural networks backbone have great potential for the nested named entity recognition (NER) problem. However, they face problems such as degenerating when positive instances and negative instances largely overlap. Besides, the generalization ability matters a lot in nested NER, as a large proportion of entities in the test set hardly appear in the training set. In this work, we try to improve the span representation by utilizing retrieval-based span-level graphs, connecting spans and entities in the training data based on n-gram features. Specifically, we build the entity-entity graph and span-entity graph globally based on n-gram similarity to integrate the information of similar neighbor entities into the span representation. To evaluate our method, we conduct experiments on three common nested NER datasets, ACE2004, ACE2005, and GENIA datasets. Experimental results show that our method achieves general improvements on all three benchmarks (+0.30 \sim 0.85 micro-F1), and obtains special superiority on low frequency entities (+0.56 \sim 2.08 recall).
CogTaskonomy: Cognitively Inspired Task Taxonomy Is Beneficial to Transfer Learning in NLP
Authors: Yifei Luo, Minghui Xu, Deyi Xiong
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Sentence, Transfer Learning

Is there a principle to guide transfer learning across tasks in natural language processing (NLP)? Taxonomy (Zamir et al., 2018) finds that a structure exists among visual tasks, as a principle underlying transfer learning for them. In this paper, we propose a cognitively inspired framework, CogTaskonomy, to learn taxonomy for NLP tasks. The framework consists of Cognitive Representation Analytics (CRA) and Cognitive-Neural Mapping (CNM). The former employs Representational Similarity Analysis, which is commonly used in computational neuroscience to find a correlation between brain-activity measurement and computational modeling, to estimate task similarity with task-specific sentence representations. The latter learns to detect task relations by projecting neural representations from NLP models to cognitive signals (i.e., fMRI voxels). Experiments on 12 NLP tasks, where BERT/TinyBERT are used as the underlying models for transfer learning, demonstrate that the proposed CogTaxonomy is able to guide transfer learning, achieving performance competitive to the Analytic Hierarchy Process (Saaty, 1987) used in visual Taskonomy (Zamir et al., 2018) but without requiring exhaustive pairwise O(m^2) task transferring. Analyses further discover that CNM is capable of learning model-agnostic task taxonomy.
RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining
Authors: Hui Su, Weiwei Shi, Xiaoyu Shen, Zhou Xiao, Tuo ji, Jiarui Fang, Jie zhou
Date: 2022-05-01 00:00:00
Proceeding: Conference 2022 5
Tasks: Contrastive Learning

Large-scale pretrained language models have achieved SOTA results on NLP tasks. However, they have been shown vulnerable to adversarial attacks especially for logographic languages like Chinese. In this work, we propose RoCBert: a pretrained Chinese Bert that is robust to various forms of adversarial attacks like word perturbation, synonyms, typos, etc. It is pretrained with the contrastive learning objective which maximizes the label consistency under different synthesized adversarial examples. The model takes as input multimodal information including the semantic, phonetic and visual features. We show all these features areimportant to the model robustness since the attack can be performed in all the three forms. Across 5 Chinese NLU tasks, RoCBert outperforms strong baselines under three blackbox adversarial algorithms without sacrificing the performance on clean testset. It also performs the best in the toxic content detection task under human-made attacks.
ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation
Authors: Chang Liu, Xu Tan, Chongyang Tao, Zhenxin Fu, Dongyan Zhao, Tie-Yan Liu, Rui Yan
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Chatbot, Dialogue Generation, Response Generation

Typical generative dialogue models utilize the dialogue history to generate the response. However, since one dialogue utterance can often be appropriately answered by multiple distinct responses, generating a desired response solely based on the historical information is not easy. Intuitively, if the chatbot can foresee in advance what the user would talk about (i.e., the dialogue future) after receiving its response, it could possibly provide a more informative response. Accordingly, we propose a novel dialogue generation framework named ProphetChat that utilizes the simulated dialogue futures in the inference phase to enhance response generation. To enable the chatbot to foresee the dialogue future, we design a beam-search-like roll-out strategy for dialogue future simulation using a typical dialogue generation model and a dialogue selector. With the simulated futures, we then utilize the ensemble of a history-to-response generator and a future-to-response generator to jointly generate a more informative response. Experiments on two popular open-domain dialogue datasets demonstrate that ProphetChat can generate better responses over strong baselines, which validates the advantages of incorporating the simulated dialogue futures.
Multi-Granularity Structural Knowledge Distillation for Language Model Compression
Authors: Chang Liu, Chongyang Tao, Jiazhan Feng, Dongyan Zhao
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Knowledge Distillation, Language Modeling, Language Modelling, Model Compression, Triplet

Transferring the knowledge to a small model through distillation has raised great interest in recent years. Prevailing methods transfer the knowledge derived from mono-granularity language units (e.g., token-level or sample-level), which is not enough to represent the rich semantics of a text and may lose some vital knowledge. Besides, these methods form the knowledge as individual representations or their simple dependencies, neglecting abundant structural relations among intermediate representations. To overcome the problems, we present a novel knowledge distillation framework that gathers intermediate representations from multiple semantic granularities (e.g., tokens, spans and samples) and forms the knowledge as more sophisticated structural relations specified as the pair-wise interactions and the triplet-wise geometric angles based on multi-granularity representations. Moreover, we propose distilling the well-organized multi-granularity structural knowledge to the student hierarchically across layers. Experimental results on GLUE benchmark demonstrate that our method outperforms advanced distillation methods.
Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts
Authors: Yue Guo, Yi Yang, Ahmed Abbasi
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Fairness

Human-like biases and undesired social stereotypes exist in large pretrained language models. Given the wide adoption of these models in real-world applications, mitigating such biases has become an emerging and important task. In this paper, we propose an automatic method to mitigate the biases in pretrained language models. Different from previous debiasing work that uses external corpora to fine-tune the pretrained models, we instead directly probe the biases encoded in pretrained models through prompts. Specifically, we propose a variant of the beam search method to automatically search for biased prompts such that the cloze-style completions are the most different with respect to different demographic groups. Given the identified biased prompts, we then propose a distribution alignment loss to mitigate the biases. Experiment results on standard datasets and metrics show that our proposed Auto-Debias approach can significantly reduce biases, including gender and racial bias, in pretrained language models such as BERT, RoBERTa and ALBERT. Moreover, the improvement in fairness does not decrease the language models’ understanding abilities, as shown using the GLUE benchmark.
Semi-supervised Domain Adaptation for Dependency Parsing with Dynamic Matching Network
Authors: Ying Li, Shuaike Li, Min Zhang
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Dependency Parsing, Domain Adaptation, Semi-supervised Domain Adaptation

Supervised parsing models have achieved impressive results on in-domain texts. However, their performances drop drastically on out-of-domain texts due to the data distribution shift. The shared-private model has shown its promising advantages for alleviating this problem via feature separation, whereas prior works pay more attention to enhance shared features but neglect the in-depth relevance of specific ones. To address this issue, we for the first time apply a dynamic matching network on the shared-private model for semi-supervised cross-domain dependency parsing. Meanwhile, considering the scarcity of target-domain labeled data, we leverage unlabeled data from two aspects, i.e., designing a new training strategy to improve the capability of the dynamic matching network and fine-tuning BERT to obtain domain-related contextualized representations. Experiments on benchmark datasets show that our proposed model consistently outperforms various baselines, leading to new state-of-the-art results on all domains. Detailed analysis on different matching strategies demonstrates that it is essential to learn suitable matching weights to emphasize useful features and ignore useless or even harmful ones. Besides, our proposed model can be directly extended to multi-source domain adaptation and achieves best performances among various baselines, further verifying the effectiveness and robustness.
There’s a Time and Place for Reasoning Beyond the Image
Authors: Xingyu Fu, Ben Zhou, Ishaan Chandratreya, Carl Vondrick, Dan Roth
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: 16k, Articles, Image Clustering

Images are often more significant than only the pixels to human eyes, as we can infer, associate, and reason with contextual information from other sources to establish a more complete picture. For example, in Figure 1, we can find a way to identify the news articles related to the picture through segment-wise understandings of the signs, the buildings, the crowds, and more. This reasoning could provide the time and place the image was taken, which will help us in subsequent tasks, such as automatic storyline construction, correction of image source in intended effect photographs, and upper-stream processing such as image clustering for certain location or time.In this work, we formulate this problem and introduce TARA: a dataset with 16k images with their associated news, time, and location, automatically extracted from New York Times, and an additional 61k examples as distant supervision from WIT. On top of the extractions, we present a crowdsourced subset in which we believe it is possible to find the images’ spatio-temporal information for evaluation purpose. We show that there exists a 70% gap between a state-of-the-art joint model and human performance, which is slightly filled by our proposed model that uses segment-wise reasoning, motivating higher-level vision-language joint models that can conduct open-ended reasoning with world knowledge.The data and code are publicly available at https://github.com/zeyofu/TARA.
Multimodal fusion via cortical network inspired losses
Authors: Shiv Shankar
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Emotion Recognition, Image Description, Multimodal Sentiment Analysis, Sentiment Analysis, speech-recognition, Speech Recognition

Information integration from different modalities is an active area of research. Human beings and, in general, biological neural systems are quite adept at using a multitude of signals from different sensory perceptive fields to interact with the environment and each other. Recent work in deep fusion models via neural networks has led to substantial improvements over unimodal approaches in areas like speech recognition, emotion recognition and analysis, captioning and image description. However, such research has mostly focused on architectural changes allowing for fusion of different modalities while keeping the model complexity manageable.Inspired by neuroscientific ideas about multisensory integration and processing, we investigate the effect of introducing neural dependencies in the loss functions. Experiments on multimodal sentiment analysis tasks with different models show that our approach provides a consistent performance boost.
WatClaimCheck: A new Dataset for Claim Entailment and Inference
Authors: Kashif Khan, Ruizhe Wang, Pascal Poupart
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Articles, Fact Checking, Passage Retrieval, Retrieval

We contribute a new dataset for the task of automated fact checking and an evaluation of state of the art algorithms. The dataset includes claims (from speeches, interviews, social media and news articles), review articles published by professional fact checkers and premise articles used by those professional fact checkers to support their review and verify the veracity of the claims. An important challenge in the use of premise articles is the identification of relevant passages that will help to infer the veracity of a claim. We show that transferring a dense passage retrieval model trained with review articles improves the retrieval quality of passages in premise articles. We report results for the prediction of claim veracity by inference from premise articles.
FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation
Authors: Moussa Kamal Eddine, Guokan Shang, Antoine Tixier, Michalis Vazirgiannis
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Text Generation

Fast and reliable evaluation metrics are key to R&D progress. While traditional natural language generation metrics are fast, they are not very reliable. Conversely, new metrics based on large pretrained language models are much more reliable, but require significant computational resources. In this paper, we propose FrugalScore, an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance. Experiments with BERTScore and MoverScore on summarization and translation show that FrugalScore is on par with the original metrics (and sometimes better), while having several orders of magnitude less parameters and running several times faster. On average over all learned metrics, tasks, and variants, FrugalScore retains 96.8% of the performance, runs 24 times faster, and has 35 times less parameters than the original metrics. We make our trained metrics publicly available, to benefit the entire NLP community and in particular researchers and practitioners with limited resources.
Tackling Fake News Detection by Continually Improving Social Context Representations using Graph Neural Networks
Authors: Nikhil Mehta, Maria Pacheco, Dan Goldwasser
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Articles, Fake News Detection, Misinformation

Easy access, variety of content, and fast widespread interactions are some of the reasons making social media increasingly popular. However, this rise has also enabled the propagation of fake news, text published by news sources with an intent to spread misinformation and sway beliefs. Detecting it is an important and challenging problem to prevent large scale misinformation and maintain a healthy society. We view fake news detection as reasoning over the relations between sources, articles they publish, and engaging users on social media in a graph framework. After embedding this information, we formulate inference operators which augment the graph edges by revealing unobserved interactions between its elements, such as similarity between documents’ contents and users’ engagement patterns. Our experiments over two challenging fake news detection tasks show that using inference operators leads to a better understanding of the social media framework enabling fake news spread, resulting in improved performance.
Understanding Gender Bias in Knowledge Base Embeddings
Authors: Yupei Du, Qi Zheng, Yuanbin Wu, Man Lan, Yan Yang, Meirong Ma
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5

Knowledge base (KB) embeddings have been shown to contain gender biases. In this paper, we study two questions regarding these biases: how to quantify them, and how to trace their origins in KB? Specifically, first, we develop two novel bias measures respectively for a group of person entities and an individual person entity. Evidence of their validity is observed by comparison with real-world census data. Second, we use the influence function to inspect the contribution of each triple in KB to the overall group bias. To exemplify the potential applications of our study, we also present two strategies (by adding and removing KB triples) to mitigate gender biases in KB embeddings.
Bias Mitigation in Machine Translation Quality Estimation
Authors: Hanna Behnke, Marina Fomicheva, Lucia Specia
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Binary Classification, Machine Translation, Translation

Machine Translation Quality Estimation (QE) aims to build predictive models to assess the quality of machine-generated translations in the absence of reference translations. While state-of-the-art QE models have been shown to achieve good results, they over-rely on features that do not have a causal impact on the quality of a translation. In particular, there appears to be a partial input bias, i.e., a tendency to assign high-quality scores to translations that are fluent and grammatically correct, even though they do not preserve the meaning of the source. We analyse the partial input bias in further detail and evaluate four approaches to use auxiliary tasks for bias mitigation. Two approaches use additional data to inform and support the main task, while the other two are adversarial, actively discouraging the model from learning the bias. We compare the methods with respect to their ability to reduce the partial input bias while maintaining the overall performance. We find that training a multitask architecture with an auxiliary binary classification task that utilises additional augmented data best achieves the desired effects and generalises well to different languages and quality metrics.