pwc-archive/papers-with-abstracts huggingface dataset:
D2GCLF: Document-to-Graph Classifier for Legal Document Classification
Authors: Qiqi Wang, Kaiqi Zhao, Robert Amor, Benjamin Liu, Ruofan Wang
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Classification, Document Classification, Graph Attention, Relation

Legal document classification is an essential task in law intelligence to automate the labor-intensive law case filing process. Unlike traditional document classification problems, legal documents should be classified by reasons and facts instead of topics. We propose a Document-to-Graph Classifier (D2GCLF), which extracts facts as relations between key participants in the law case and represents a legal document with four relation graphs. Each graph is responsible for capturing different relations between the litigation participants. We further develop a graph attention network on top of the four relation graphs to classify the legal documents. Experiments on a real-world legal document dataset show that D2GCLF outperforms the state-of-the-art methods in terms of accuracy.
A Label-Aware Autoregressive Framework for Cross-Domain NER
Authors: Jinpeng Hu, He Zhao, Dan Guo, Xiang Wan, Tsung-Hui Chang
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Cross-Domain Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Cross-domain named entity recognition (NER) aims to borrow the entity information from the source domain to help the entity recognition in the target domain with limited labeled data. Despite the promising performance of existing approaches, most of them focus on reducing the discrepancy of token representation between source and target domains, while the transfer of the valuable label information is often not explicitly considered or even ignored. Therefore, we propose a novel autoregressive framework to advance cross-domain NER by first enhancing the relationship between labels and tokens and then further improving the transferability of label information. Specifically, we associate each label with an embedding vector, and for each token, we utilize a bidirectional LSTM (Bi-LSTM) to encode the labels of its previous tokens for modeling internal context information and label dependence. Afterward, we propose a Bi-Attention module that merges the token representation from a pre-trained model and the label features from the Bi-LSTM as the label-aware information, which is concatenated to the token representation to facilitate cross-domain NER. In doing so, label information contained in the embedding vectors can be effectively transferred to the target domain, and Bi-LSTM can further model the label relationship among different domains by pre-train and then fine-tune setting. Experimental results on several datasets confirm the effectiveness of our model, where our model achieves significant improvements over the state of the arts.
A Dog Is Passing Over The Jet? A Text-Generation Dataset for Korean Commonsense Reasoning and Evaluation
Authors: Jaehyung Seo, Seounghoon Lee, Chanjun Park, Yoonna Jang, Hyeonseok Moon, Sugyeong Eo, Seonmin Koo, Heuiseok Lim
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Language Model Evaluation, Language Modeling, Language Modelling, Natural Language Understanding, Sentence, Text Generation

Recent natural language understanding (NLU) research on the Korean language has been vigorously maturing with the advancements of pretrained language models and datasets. However, Korean pretrained language models still struggle to generate a short sentence with a given condition based on compositionality and commonsense reasoning (i.e., generative commonsense reasoning). The two major challenges are inadequate data resources to develop generative commonsense reasoning regarding Korean linguistic features and to evaluate language models which are necessary for natural language generation (NLG). To solve these problems, we propose a text-generation dataset for Korean generative commonsense reasoning and language model evaluation. In this work, a semi-automatic dataset construction approach filters out contents inexplicable to commonsense, ascertains quality, and reduces the cost of building the dataset. We also present an in-depth analysis of the generation results of language models with various evaluation metrics along with human-annotated scores. The whole dataset is publicly available at (https://aihub.or.kr/opendata/korea-university).
Analytical Reasoning of Text
Authors: Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Yining Chen, Jiahai Wang, Jian Yin, Ming Zhou, Nan Duan
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7

Analytical reasoning is an essential and challenging task that requires a system to analyze a scenario involving a set of particular circumstances and perform reasoning over it to make conclusions. However, current neural models with implicit reasoning ability struggle to solve this task. In this paper, we study the challenge of analytical reasoning of text and collect a new dataset consisting of questions from the Law School Admission Test from 1991 to 2016. We analyze what knowledge understanding and reasoning abilities are required to do well on this task, and present an approach dubbed ARM. It extracts knowledge such as participants and facts from the context. Such knowledge are applied to an inference engine to deduce legitimate solutions for drawing conclusions. In our experiments, we find that ubiquitous pre-trained models struggle to deal with this task as their performance is close to random guess. Results show that ARM outperforms pre-trained models significantly. Moreover, we demonstrate that ARM has better explicit interpretable reasoning ability.
Jointly Learning Guidance Induction and Faithful Summary Generation via Conditional Variational Autoencoders
Authors: Wang Xu, Tiejun Zhao
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Abstractive Text Summarization

Abstractive summarization can generate high quality results with the development of the neural network. However, generating factual consistency summaries is a challenging task for abstractive summarization. Recent studies extract the additional information with off-the-shelf tools from the source document as a clue to guide the summary generation, which shows effectiveness to improve the faithfulness. Unlike these work, we present a novel framework based on conditional variational autoencoders, which induces the guidance information and generates the summary equipment with the guidance synchronously. Experiments on XSUM and CNNDM dataset show that our approach can generate relevant and fluent summaries which is more faithful than the existing state-of-the-art approaches, according to multiple factual consistency metrics.
Unsupervised Domain Adaptation for Question Generation with DomainData Selection and Self-training
Authors: Peide Zhu, Claudia Hauff
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Domain Adaptation, Question Generation, Question-Generation, Unsupervised Domain Adaptation

Question generation (QG) approaches based on large neural models require (i) large-scale and (ii) high-quality training data. These two requirements pose difficulties for specific application domains where training data is expensive and difficult to obtain. The trained QG models’ effectiveness can degrade significantly when they are applied on a different domain due to domain shift. In this paper, we explore an unsupervised domain adaptation approach to combat the lack of training data and domain shift issue with domain data selection and self-training. We first present a novel answer-aware strategy for domain data selection to select data with the most similarity to a new domain. The selected data are then used as pseudo-in-domain data to retrain the QG model. We then present generation confidence guided self-training with two generation confidence modeling methods (i) generated questions’ perplexity and (ii) the fluency score. We test our approaches on three large public datasets with different domain similarities, using a transformer-based pre-trained QG model. The results show that our proposed approaches outperform the baselines, and show the viability of unsupervised domain adaptation with answer-aware data selection and self-training on the QG task.
The Case for a Single Model that can Both Generate Continuations and Fill-in-the-Blank
Authors: Daphne Ippolito, Liam Dugan, Emily Reif, Ann Yuan, Andy Coenen, Chris Callison-Burch
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Position, Text Generation

The task of inserting text into a specified position in a passage, known as fill in the blank (FitB), is useful for a variety of applications where writers interact with a natural language generation (NLG) system to craft text. While previous work has tackled this problem with models trained specifically to do fill in the blank, a more useful model is one that can effectively perform _both_ FitB and continuation tasks. In this work, we evaluate the feasibility of using a single model to do both tasks. We show that models pre-trained with a FitB-style objective are capable of both tasks, while models pre-trained for continuation are not. Finally, we show how these models can be easily finetuned to allow for fine-grained control over the length and word choice of the generation.
Learning Discriminative Representations for Open Relation Extraction with Instance Ranking and Label Calibration
Authors: Shusen Wang, Bin Duan, Yanan Wu, Yajing Xu
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Relation, Relation Extraction

Open relation extraction is the task to extract relational facts without pre-defined relation types from open-domain corpora. However, since there are some hard or semi-hard instances sharing similar context and entity information but belonging to different underlying relation, current OpenRE methods always cluster them into the same relation type. In this paper, we propose a novel method based on Instance Ranking and Label Calibration strategies (IRLC) to learn discriminative representations for open relation extraction. Due to lacking the original instance label, we provide three surrogate strategies to generate the positive, hard negative, and semi-hard negative instances for the original instance. Instance ranking aims to refine the relational feature space by pushing the hard and semi-hard negative instances apart from the original instance with different margins and pulling the original instance and its positive instance together. To refine the cluster probability distributions of these instances, we introduce a label calibration strategy to model the constraint relationship between instances. Experimental results on two public datasets demonstrate that our proposed method can significantly outperform the previous state-of-the-art methods.
RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction
Authors: Shusen Wang, Bosen Zhang, Yajing Xu, Yanan Wu, Bo Xiao
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Contrastive Learning, Data Augmentation, Relation, Relation Classification, Relation Extraction

Zero-shot relation extraction aims to identify novel relations which cannot be observed at the training stage. However, it still faces some challenges since the unseen relations of instances are similar or the input sentences have similar entities, the unseen relation representations from different categories tend to overlap and lead to errors. In this paper, we propose a novel Relation Contrastive Learning framework (RCL) to mitigate above two types of similar problems: Similar Relations and Similar Entities. By jointly optimizing a contrastive instance loss with a relation classification loss on seen relations, RCL can learn subtle difference between instances and achieve better separation between different relation categories in the representation space simultaneously. Especially in contrastive instance learning, the dropout noise as data augmentation is adopted to amplify the semantic difference between similar instances without breaking relation representation, so as to promote model to learn more effective representations. Experiments conducted on two well-known datasets show that RCL can significantly outperform previous state-of-the-art methods. Moreover, if the seen relations are insufficient, RCL can also obtain comparable results with the model trained on the full training set, showing the robustness of our approach.
Latent Group Dropout for Multilingual and Multidomain Machine Translation
Authors: Minh-Quang Pham, François Yvon, Josep Crego
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Machine Translation, Translation

Multidomain and multilingual machine translation often rely on parameter sharing strategies, where large portions of the network are meant to capture the commonalities of the tasks at hand, while smaller parts are reserved to model the peculiarities of a language or a domain. In adapter-based approaches, these strategies are hardcoded in the network architecture, independent of the similarities between tasks. In this work, we propose a new method to better take advantage of these similarities, using a latent-variable model. We also develop new techniques to train this model end-to-end and report experimental results showing that the learned patterns are both meaningful and yield improved translation performance without any increase of the model size.
Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based Prediction
Authors: Senhui Zhang, Tao Ji, Wendi Ji, Xiaoling Wang
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Contrastive Learning, Event Detection

Event detection is a classic natural language processing task. However, the constantly emerging new events make supervised methods not applicable to unseen types. Previous zero-shot event detection methods either require predefined event types as heuristic rules or resort to external semantic analyzing tools. To overcome this weakness, we propose an end-to-end framework named Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based Prediction (ZEOP). By creatively introducing multiple contrastive samples with ordered similarities, the encoder can learn event representations from both instance-level and class-level, which makes the distinctions between different unseen types more significant. Meanwhile, we utilize the prompt-based prediction to identify trigger words without relying on external resources. Experiments demonstrate that our model detects events more effectively and accurately than state-of-the-art methods.
Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine Translation
Authors: Huan Lin, Baosong Yang, Liang Yao, Dayiheng Liu, Haibo Zhang, Jun Xie, Min Zhang, Jinsong Su
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Diversity, Machine Translation, NMT, Sentence

Diverse NMT aims at generating multiple diverse yet faithful translations given a source sentence. In this paper, we investigate a common shortcoming in existing diverse NMT studies: the model is usually trained with single reference, while expected to generate multiple candidate translations in inference. The discrepancy between training and inference enlarges the confidence variance and quality gap among candidate translations and thus hinders model performance. To deal with this defect, we propose a multi-candidate optimization framework for diverse NMT. Specifically, we define assessments to score the diversity and the quality of candidate translations during training, and optimize the diverse NMT model with two strategies based on reinforcement learning, namely hard constrained training and soft constrained training. We conduct experiments on NIST Chinese-English and WMT14 English-German translation tasks. The results illustrate that our framework is transparent to basic diverse NMT models, and universally makes better trade-off between diversity and quality. Our source codeis available at https://github.com/DeepLearnXMU/MultiCanOptim.
EA^2E: Improving Consistency with Event Awareness for Document-Level Argument Extraction
Authors: Qi Zeng, Qiusi Zhan, Heng Ji
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Event Argument Extraction, Knowledge Base Population, Question Answering

Events are inter-related in documents. Motivated by the one-sense-per-discourse theory, we hypothesize that a participant tends to play consistent roles across multiple events in the same document. However recent work on document-level event argument extraction models each individual event in isolation and therefore causes inconsistency among extracted arguments across events, which will further cause discrepancy for downstream applications such as event knowledge base population, question answering, and hypothesis generation. In this work, we formulate event argument consistency as the constraints from event-event relations under the document-level setting. To improve consistency we introduce the Event-Aware Argument Extraction (EA^2E) model with augmented context for training and inference. Experiment results on WIKIEVENTS and ACE2005 datasets demonstrate the effectiveness of EA^2E compared to baseline methods.
Label Refinement via Contrastive Learning for Distantly-Supervised Named Entity Recognition
Authors: Huaiyuan Ying, Shengxuan Luo, Tiantian Dang, Sheng Yu
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Contrastive Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Distantly-supervised named entity recognition (NER) locates and classifies entities using only knowledge bases and unlabeled corpus to mitigate the reliance on human-annotated labels. The distantly annotated data suffer from the noise in labels, and previous works on DSNER have proved the importance of pre-refining distant labels with hand-crafted rules and extra existing semantic information. In this work, we explore the way to directly learn the distant label refinement knowledge by imitating annotations of different qualities and comparing these annotations in contrastive learning frameworks. the proposed distant label refinement model can give modified suggestions on distant data without additional supervised labels, and thus reduces the requirement on the quality of the knowledge bases. We perform extensive experiments and observe that recent and state-of-the-art DSNER methods gain evident benefits with our method.
RoViST: Learning Robust Metrics for Visual Storytelling
Authors: Eileen Wang, Caren Han, Josiah Poon
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Sentence, Text Generation, Visual Grounding, Visual Storytelling

Visual storytelling (VST) is the task of generating a story paragraph that describes a given image sequence. Most existing storytelling approaches have evaluated their models using traditional natural language generation metrics like BLEU or CIDEr. However, such metrics based on n-gram matching tend to have poor correlation with human evaluation scores and do not explicitly consider other criteria necessary for storytelling such as sentence structure or topic coherence. Moreover, a single score is not enough to assess a story as it does not inform us about what specific errors were made by the model. In this paper, we propose 3 evaluation metrics sets that analyses which aspects we would look for in a good story: 1) visual grounding, 2) coherence, and 3) non-redundancy. We measure the reliability of our metric sets by analysing its correlation with human judgement scores on a sample of machine stories obtained from 4 state-of-the-arts models trained on the Visual Storytelling Dataset (VIST). Our metric sets outperforms other metrics on human correlation, and could be served as a learning based evaluation metric set that is complementary to existing rule-based metrics.
ID10M: Idiom Identification in 10 Languages
Authors: Simone Tedeschi, Federico Martelli, Roberto Navigli
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Natural Language Understanding

Idioms are phrases which present a figurative meaning that cannot be (completely) derived by looking at the meaning of their individual components.Identifying and understanding idioms in context is a crucial goal and a key challenge in a wide range of Natural Language Understanding tasks. Although efforts have been undertaken in this direction, the automatic identification and understanding of idioms is still a largely under-investigated area, especially when operating in a multilingual scenario. In this paper, we address such limitations and put forward several new contributions: we propose a novel multilingual Transformer-based system for the identification of idioms; we produce a high-quality automatically-created training dataset in 10 languages, along with a novel manually-curated evaluation benchmark; finally, we carry out a thorough performance analysis and release our evaluation suite at https://github.com/Babelscape/ID10M.
Cross-Domain Classification of Moral Values
Authors: Enrico Liscio, Alin Dondera, Andrei Geadau, Catholijn Jonker, Pradeep Murukannaiah
Date: 2022-07-01 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Classification, domain classification

Moral values influence how we interpret and act upon the information we receive. Identifying human moral values is essential for artificially intelligent agents to co-exist with humans. Recent progress in natural language processing allows the identification of moral values in textual discourse. However, domain-specific moral rhetoric poses challenges for transferring knowledge from one domain to another.We provide the first extensive investigation on the effects of cross-domain classification of moral values from text. We compare a state-of-the-art deep learning model (BERT) in seven domains and four cross-domain settings. We show that a value classifier can generalize and transfer knowledge to novel domains, but it can introduce catastrophic forgetting. We also highlight the typical classification errors in cross-domain value classification and compare the model predictions to the annotators agreement. Our results provide insights to computer and social scientists that seek to identify moral rhetoric specific to a domain of discourse.
A Systematic Survey of Text Worlds as Embodied Natural Language Environments
Authors: Peter Jansen
Date: 2022-07-01 00:00:00
Proceeding: NAACL (Wordplay) 2022 7
Tasks: Common Sense Reasoning, Knowledge Graphs, Transfer Learning

Text Worlds are virtual environments for embodied agents that, unlike 2D or 3D environments, are rendered exclusively using textual descriptions. These environments offer an alternative to higher-fidelity 3D environments due to their low barrier to entry, providing the ability to study semantics, compositional inference, and other high-level tasks with rich action spaces while controlling for perceptual input. This systematic survey outlines recent developments in tooling, environments, and agent modeling for Text Worlds, while examining recent trends in knowledge graphs, common sense reasoning, transfer learning of Text World performance to higher-fidelity environments, as well as near-term development targets that, once achieved, make Text Worlds an attractive general research paradigm for natural language processing.