pwc-archive/papers-with-abstracts huggingface dataset:
Generative Cross-Domain Data Augmentation for Aspect and Opinion Co-Extraction
Authors: Junjie Li, Jianfei Yu, Rui Xia
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Data Augmentation, Domain Adaptation, Opinion Mining, Sentence, Unsupervised Domain Adaptation

As a fundamental task in opinion mining, aspect and opinion co-extraction aims to identify the aspect terms and opinion terms in reviews. However, due to the lack of fine-grained annotated resources, it is hard to train a robust model for many domains. To alleviate this issue, unsupervised domain adaptation is proposed to transfer knowledge from a labeled source domain to an unlabeled target domain. In this paper, we propose a new Generative Cross-Domain Data Augmentation framework for unsupervised domain adaptation. The proposed framework is aimed to generate target-domain data with fine-grained annotation by exploiting the labeled data in the source domain. Specifically, we remove the domain-specific segments in a source-domain labeled sentence, and then use this as input to a pre-trained sequence-to-sequence model BART to simultaneously generate a target-domain sentence and predict the corresponding label for each word. Experimental results on three datasets demonstrate that our approach is more effective than previous domain adaptation methods.
Grapheme-to-Phoneme Conversion for Thai using Neural Regression Models
Authors: Tomohiro Yamasaki
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Decoder, Grapheme-to-Phoneme Conversion, regression

We propose a novel Thai grapheme-to-phoneme conversion method based on a neural regression model that is trained using neural networks to predict the similarity between a candidate and the correct pronunciation. After generating a set of candidates for an input word or phrase using the orthography rules, this model selects the best-similarity pronunciation from the candidates. This method can be applied to languages other than Thai simply by preparing enough orthography rules, and can reduce the mistakes that neural network models often make. We show that the accuracy of the proposed method is .931, which is comparable to that of encoder-decoder sequence models. We also demonstrate that the proposed method is superior in terms of the difference between correct and predicted pronunciations because incorrect, strange output sometimes occurs when using encoder-decoder sequence models but the error is within the expected range when using the proposed method.
Joint Extraction of Entities, Relations, and Events via Modeling Inter-Instance and Inter-Label Dependencies
Authors: Minh Van Nguyen, Bonan Min, Franck Dernoncourt, Thien Nguyen
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Event Argument Extraction, Relation Extraction, Representation Learning

Event trigger detection, entity mention recognition, event argument extraction, and relation extraction are the four important tasks in information extraction that have been performed jointly (Joint Information Extraction - JointIE) to avoid error propagation and leverage dependencies between the task instances (i.e., event triggers, entity mentions, relations, and event arguments). However, previous JointIE models often assume heuristic manually-designed dependency between the task instances and mean-field factorization for the joint distribution of instance labels, thus unable to capture optimal dependencies among instances and labels to improve representation learning and IE performance. To overcome these limitations, we propose to induce a dependency graph among task instances from data to boost representation learning. To better capture dependencies between instance labels, we propose to directly estimate their joint distribution via Conditional Random Fields. Noise Contrastive Estimation is introduced to address the maximization of the intractable joint likelihood for model training. Finally, to improve the decoding with greedy or beam search in prior work, we present Simulated Annealing to better find the globally optimal assignment for instance labels at decoding time. Experimental results show that our proposed model outperforms previous models on multiple IE tasks across 5 datasets and 2 languages.
SURF: Semantic-level Unsupervised Reward Function for Machine Translation
Authors: Atijit Anuchitanukul, Julia Ive
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Diversity, Machine Translation, Reinforcement Learning (RL), Semantic Similarity, Semantic Textual Similarity, Sentence, Translation

The performance of Reinforcement Learning (RL) for natural language tasks including Machine Translation (MT) is crucially dependent on the reward formulation. This is due to the intrinsic difficulty of the task in the high-dimensional discrete action space as well as the sparseness of the standard reward functions defined for limited set of ground-truth sequences biased towards singular lexical choices. To address this issue, we formulate SURF, a maximally dense semantic-level unsupervised reward function which mimics human evaluation by considering both sentence fluency and semantic similarity. We demonstrate the strong potential of SURF to leverage a family of Actor-Critic Transformer-based Architectures with synchronous and asynchronous multi-agent variants. To tackle the problem of large action-state spaces, each agent is equipped with unique exploration strategies, promoting diversity during its exploration of the hypothesis space. When BLEU scores are compared, our dense unsupervised reward outperforms the standard sparse reward by 2% on average for in- and out-of-domain settings.
Disentangling Categorization in Multi-agent Emergent Communication
Authors: Washington Garcia, Hamilton Clouse, Kevin Butler
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Representation Learning

The emergence of language between artificial agents is a recent focus of computational linguistics, as it offers a synthetic substrate for reasoning about human language evolution. From the perspective of cognitive science, sophisticated categorization in humans is thought to enable reasoning about novel observations, and thus compose old information to describe new phenomena. Unfortunately, the literature to date has not managed to isolate the effect of categorization power in artificial agents on their inter-communication ability, particularly on novel, unseen objects. In this work, we propose the use of disentangled representations from representation learning to quantify the categorization power of agents, enabling a differential analysis between combinations of heterogeneous systems, e.g., pairs of agents which learn to communicate despite mismatched concept realization. Through this approach, we observe that agent heterogeneity can cut signaling accuracy by up to 40%, despite encouraging compositionality in the artificial language. We conclude that the reasoning process of agents plays a key role in their communication, with unexpected benefits arising from their mixing, such as better language compositionality.
Show, Don’t Tell: Demonstrations Outperform Descriptions for Schema-Guided Task-Oriented Dialogue
Authors: Raghav Gupta, Harrison Lee, Jeffrey Zhao, Yuan Cao, Abhinav Rastogi, Yonghui Wu
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Dialogue State Tracking, Zero-shot Generalization

Building universal dialogue systems that operate across multiple domains/APIs and generalize to new ones with minimal overhead is a critical challenge. Recent works have leveraged natural language descriptions of schema elements to enable such systems; however, descriptions only indirectly convey schema semantics. In this work, we propose Show, Don’t Tell, which prompts seq2seq models with a labeled example dialogue to show the semantics of schema elements rather than tell the model through descriptions. While requiring similar effort from service developers as generating descriptions, we show that using short examples as schema representations with large language models results in state-of-the-art performance on two popular dialogue state tracking benchmarks designed to measure zero-shot generalization - the Schema-Guided Dialogue dataset and the MultiWOZ leave-one-out benchmark.
Learning to Generate Examples for Semantic Processing Tasks
Authors: Danilo Croce, Simone Filice, Giuseppe Castellucci, Roberto Basili
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Data Augmentation, Natural Language Inference, text-classification, Text Classification

Even if recent Transformer-based architectures, such as BERT, achieved impressive results in semantic processing tasks, their fine-tuning stage still requires large scale training resources. Usually, Data Augmentation (DA) techniques can help to deal with low resource settings. In Text Classification tasks, the objective of DA is the generation of well-formed sentences that i) represent the desired task category and ii) are novel with respect to existing sentences. In this paper, we propose a neural approach to automatically learn to generate new examples using a pre-trained sequence-to-sequence model. We first learn a task-oriented similarity function that we use to pair similar examples. Then, we use these example pairs to train a model to generate examples. Experiments in low resource settings show that augmenting the training material with the proposed strategy systematically improves the results on text classification and natural language inference tasks by up to 10% accuracy, outperforming existing DA approaches.
Entity Linking via Explicit Mention-Mention Coreference Modeling
Authors: Dhruv Agarwal, Rico Angell, Nicholas Monath, Andrew McCallum
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Entity Linking, Re-Ranking

Learning representations of entity mentions is a core component of modern entity linking systems for both candidate generation and making linking predictions. In this paper, we present and empirically analyze a novel training approach for learning mention and entity representations that is based on building minimum spanning arborescences (i.e., directed spanning trees) over mentions and entities across documents to explicitly model mention coreference relationships. We demonstrate the efficacy of our approach by showing significant improvements in both candidate generation recall and linking accuracy on the Zero-Shot Entity Linking dataset and MedMentions, the largest publicly available biomedical dataset. In addition, we show that our improvements in candidate generation yield higher quality re-ranking models downstream, setting a new SOTA result in linking accuracy on MedMentions. Finally, we demonstrate that our improved mention representations are also effective for the discovery of new entities via cross-document coreference.
Disentangling Indirect Answers to Yes-No Questions in Real Conversations
Authors: Krishna Sanagavarapu, Jathin Singaraju, Anusha Kakileti, Anirudh Kaza, Aaron Mathews, Helen Li, Nathan Brito, Eduardo Blanco
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7

In this paper, we explore the task of determining indirect answers to yes-no questions in real conversations. We work with transcripts of phone conversations in the Switchboard Dialog Act (SwDA) corpus and create SwDA-IndirectAnswers (SwDA-IA), a subset of SwDA consisting of all conversations containing a yes-no question with an indirect answer. We annotate the underlying direct answers to the yes-no questions (yes, probably yes, middle, probably no, or no). We show that doing so requires taking into account conversation context: the indirect answer alone is insufficient to determine the ground truth. Experimental results also show that taking into account context is beneficial. More importantly, our results demonstrate that existing corpora with synthetic indirect answers to yes-no questions are not beneficial when working with real conversations. Our best models outperform the majority baseline by a substantial margin, but the task remains a challenge (F1: 0.46).
Partial-input baselines show that NLI models can ignore context, but they don’t.
Authors: Neha Srikanth, Rachel Rudinger
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Sensitivity

When strong partial-input baselines reveal artifacts in crowdsourced NLI datasets, the performance of full-input models trained on such datasets is often dismissed as reliance on spurious correlations. We investigate whether state-of-the-art NLI models are capable of overriding default inferences made by a partial-input baseline. We introduce an evaluation set of 600 examples consisting of perturbed premises to examine a RoBERTa model’s sensitivity to edited contexts. Our results indicate that NLI models are still capable of learning to condition on context—a necessary component of inferential reasoning—despite being trained on artifact-ridden datasets.
Reducing Disambiguation Biases in NMT by Leveraging Explicit Word Sense Information
Authors: Niccolò Campolungo, Tommaso Pasini, Denis Emelin, Roberto Navigli
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Machine Translation, NMT, Translation

Recent studies have shed some light on a common pitfall of Neural Machine Translation (NMT) models, stemming from their struggle to disambiguate polysemous words without lapsing into their most frequently occurring senses in the training corpus.In this paper, we first provide a novel approach for automatically creating high-precision sense-annotated parallel corpora, and then put forward a specifically tailored fine-tuning strategy for exploiting these sense annotations during training without introducing any additional requirement at inference time.The use of explicit senses proved to be beneficial to reduce the disambiguation bias of a baseline NMT model, while, at the same time, leading our system to attain higher BLEU scores than its vanilla counterpart in 3 language pairs.
Interactive Symbol Grounding with Complex Referential Expressions
Authors: Rimvydas Rubavicius, Alex Lascarides
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Few-Shot Learning, Negation

We present a procedure for learning to ground symbols from a sequence of stimuli consisting of an arbitrarily complex noun phrase (e.g. “all but one green square above both red circles.”) and its designation in the visual scene. Our distinctive approach combines: a) lazy few-shot learning to relate open-class words like green and above to their visual percepts; and b) symbolic reasoning with closed-class word categories like quantifiers and negation. We use this combination to estimate new training examples for grounding symbols that occur within a noun phrase but aren’t designated by that noun phase (e.g, red in the above example), thereby potentially gaining data efficiency. We evaluate the approach in a visual reference resolution task, in which the learner starts out unaware of concepts that are part of the domain model and how they relate to visual percepts.
SSEGCN: Syntactic and Semantic Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis
Authors: Zheng Zhang, Zili Zhou, Yanna Wang
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentence, Sentiment Analysis

Aspect-based Sentiment Analysis (ABSA) aims to predict the sentiment polarity towards a particular aspect in a sentence. Recently, graph neural networks based on dependency tree convey rich structural information which is proven to be utility for ABSA. However, how to effectively harness the semantic and syntactic structure information from the dependency tree remains a challenging research question. In this paper, we propose a novel Syntactic and Semantic Enhanced Graph Convolutional Network (SSEGCN) model for ABSA task. Specifically, we propose an aspect-aware attention mechanism combined with self-attention to obtain attention score matrices of a sentence, which can not only learn the aspect-related semantic correlations, but also learn the global semantics of the sentence. In order to obtain comprehensive syntactic structure information, we construct syntactic mask matrices of the sentence according to the different syntactic distances between words. Furthermore, to combine syntactic structure and semantic information, we equip the attention score matrices by syntactic mask matrices. Finally, we enhance the node representations with graph convolutional network over attention score matrices for ABSA. Experimental results on benchmark datasets illustrate that our proposed model outperforms state-of-the-art methods.
DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks
Authors: Lin Tian, Xiuzhen Zhang, Jey Han Lau
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Graph Attention, Misinformation, Rumour Detection

Social media rumours, a form of misinformation, can mislead the public and cause significant economic and social disruption. Motivated by the observation that the user network — which captures \textit{who} engage with a story — and the comment network — which captures \textit{how} they react to it — provide complementary signals for rumour detection, in this paper, we propose DUCK (rumour \underline{d}etection with \underline{u}ser and \underline{c}omment networ\underline{k}s) for rumour detection on social media. We study how to leverage transformers and graph attention networks to jointly model the contents and structure of social media conversations, as well as the network of users who engaged in these conversations. Over four widely used benchmark rumour datasets in English and Chinese, we show that DUCK produces superior performance for detecting rumours, creating a new state-of-the-art. Source code for DUCK is available at: https://github.com/l tian678/DUCK-code.
DynamicTOC: Persona-based Table of Contents for Consumption of Long Documents
Authors: Himanshu Maheshwari, Nethraa Sivakumar, Shelly Jain, Tanvi Karandikar, Vinay Aggarwal, Navita Goyal, Sumit Shekhar
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Deep Reinforcement Learning

Long documents like contracts, financial documents, etc., are often tedious to read through. Linearly consuming (via scrolling or navigation through default table of content) these documents is time-consuming and challenging. These documents are also authored to be consumed by varied entities (referred to as persona in the paper) interested in only certain parts of the document. In this work, we describe DynamicToC, a dynamic table of content-based navigator, to aid in the task of non-linear, persona-based document consumption. DynamicToC highlights sections of interest in the document as per the aspects relevant to different personas. DynamicToC is augmented with short questions to assist the users in understanding underlying content. This uses a novel deep-reinforcement learning technique to generate questions on these persona-clustered paragraphs. Human and automatic evaluations suggest the efficacy of both end-to-end pipeline and different components of DynamicToC.
Partner Personas Generation for Dialogue Response Generation
Authors: Hongyuan Lu, Wai Lam, Hong Cheng, Helen Meng
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: reinforcement-learning, Reinforcement Learning (RL), Response Generation

Incorporating personas information allows diverse and engaging responses in dialogue response generation. Unfortunately, prior works have primarily focused on self personas and have overlooked the value of partner personas. Moreover, in practical applications, the availability of the gold partner personas is often not the case. This paper attempts to tackle these issues by offering a novel framework that leverages automatic partner personas generation to enhance the succeeding dialogue response generation. Our framework employs reinforcement learning with a dedicatedly designed critic network for reward judgement. Experimental results from automatic and human evaluations indicate that our framework is capable of generating relevant, interesting, coherent and informative partner personas, even compared to the ground truth partner personas. This enhances the succeeding dialogue response generation, which surpasses our competitive baselines that condition on the ground truth partner personas.
Dual-Channel Evidence Fusion for Fact Verification over Texts and Tables
Authors: Nan Hu, Zirui Wu, Yuxuan Lai, Xiao Liu, Yansong Feng
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Fact Verification

Different from previous fact extraction and verification tasks that only consider evidence of a single format, FEVEROUS brings further challenges by extending the evidence format to both plain text and tables. Existing works convert all candidate evidence into either sentences or tables, thus often failing to fully capture the rich context in their original format from the converted evidence, let alone the context information lost during conversion. In this paper, we propose a Dual Channel Unified Format fact verification model (DCUF), which unifies various evidence into parallel streams, i.e., natural language sentences and a global evidence table, simultaneously. With carefully-designed evidence conversion and organization methods, DCUF makes the most of pre-trained table/language models to encourage each evidence piece to perform early and thorough interactions with other pieces in its original format. Experiments show that our model can make better use of existing pre-trained models to absorb evidence of two formats, thus outperforming previous works by a large margin. Our code and models are publicly available.
Syn2Vec: Synset Colexification Graphs for Lexical Semantic Similarity
Authors: John Harvill, Roxana Girju, Mark Hasegawa-Johnson
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Semantic Similarity, Semantic Textual Similarity

In this paper we focus on patterns of colexification (co-expressions of form-meaning mapping in the lexicon) as an aspect of lexical-semantic organization, and use them to build large scale synset graphs across BabelNet’s typologically diverse set of 499 world languages. We introduce and compare several approaches: monolingual and cross-lingual colexification graphs, popular distributional models, and fusion approaches. The models are evaluated against human judgments on a semantic similarity task for nine languages. Our strong empirical findings also point to the importance of universality of our graph synset embedding representations with no need for any language-specific adaptation when evaluated on the lexical similarity task. The insights of our exploratory investigation of large-scale colexification graphs could inspire significant advances in NLP across languages, especially for tasks involving languages which lack dedicated lexical resources, and can benefit from language transfer from large shared cross-lingual semantic spaces.
Is “My Favorite New Movie” My Favorite Movie? Probing the Understanding of Recursive Noun Phrases
Authors: Qing Lyu, Zheng Hua, Daoxin Li, Li Zhang, Marianna Apidianaki, Chris Callison-Burch
Date: 2022-07-01 00:00:00
Proceeding: NAACL 2022 7
Tasks: Common Sense Reasoning, Natural Language Inference

Recursive noun phrases (NPs) have interesting semantic properties. For example, “my favorite new movie” is not necessarily my favorite movie, whereas “my new favorite movie” is. This is common sense to humans, yet it is unknown whether language models have such knowledge. We introduce the Recursive Noun Phrase Challenge (RNPC), a dataset of three textual inference tasks involving textual entailment and event plausibility comparison, precisely targeting the understanding of recursive NPs. When evaluated on RNPC, state-of-the-art Transformer models only perform around chance. Still, we show that such knowledge is learnable with appropriate data. We further probe the models for relevant linguistic features that can be learned from our tasks, including modifier semantic category and modifier scope. Finally, models trained on RNPC achieve strong zero-shot performance on an extrinsic Harm Detection evaluation task, showing the usefulness of the understanding of recursive NPs in downstream applications.