NAACL
Collection
Accepted papers for NAACL (Annual Conference of the North American Chapter of the Association for Computational Linguistics), one dataset per year. • 9 items • Updated
paper_id stringlengths 17 19 | title stringlengths 18 130 | paper_url stringlengths 43 45 | authors listlengths 1 45 | abstract large_stringlengths 449 1.84k | anthology_id stringlengths 17 19 | doi stringlengths 29 31 | award stringclasses 7
values | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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2022.naacl-main.1 | Social Norms Guide Reference Resolution | https://aclanthology.org/2022.naacl-main.1/ | [
"Mitchell Abrams",
"Matthias Scheutz"
] | Humans use natural language, vision, and context to resolve referents in their environment. While some situated reference resolution is trivial, ambiguous cases arise when the language is underspecified or there are multiple candidate referents. This study investigates howpragmatic modulators external to the linguistic... | 2022.naacl-main.1 | 10.18653/v1/2022.naacl-main.1 | null | null | null |
2022.naacl-main.2 | Learning Natural Language Generation with Truncated Reinforcement Learning | https://aclanthology.org/2022.naacl-main.2/ | [
"Alice Martin",
"Guillaume Quispe",
"Charles Ollion",
"Sylvain Le Corff",
"Florian Strub",
"Olivier Pietquin"
] | This paper introduces TRUncated ReinForcement Learning for Language (TrufLL), an original approach to train conditional languagemodels without a supervised learning phase, by only using reinforcement learning (RL). As RL methods unsuccessfully scale to large action spaces, we dynamically truncate the vocabulary space u... | 2022.naacl-main.2 | 10.18653/v1/2022.naacl-main.2 | null | null | null |
2022.naacl-main.3 | Language Model Augmented Monotonic Attention for Simultaneous Translation | https://aclanthology.org/2022.naacl-main.3/ | [
"Sathish Reddy Indurthi",
"Mohd Abbas Zaidi",
"Beomseok Lee",
"Nikhil Kumar Lakumarapu",
"Sangha Kim"
] | The state-of-the-art adaptive policies for Simultaneous Neural Machine Translation (SNMT) use monotonic attention to perform read/write decisions based on the partial source and target sequences. The lack of sufficient information might cause the monotonic attention to take poor read/write decisions, which in turn nega... | 2022.naacl-main.3 | 10.18653/v1/2022.naacl-main.3 | null | null | null |
2022.naacl-main.4 | What Makes a Good and Useful Summary? Incorporating Users in Automatic Summarization Research | https://aclanthology.org/2022.naacl-main.4/ | [
"Maartje Ter Hoeve",
"Julia Kiseleva",
"Maarten de Rijke"
] | Automatic text summarization has enjoyed great progress over the years and is used in numerous applications, impacting the lives of many. Despite this development, there is little research that meaningfully investigates how the current research focus in automatic summarization aligns with users’ needs. To bridge this g... | 2022.naacl-main.4 | 10.18653/v1/2022.naacl-main.4 | null | 2012.07619 | title_snapshot |
2022.naacl-main.5 | ErAConD: Error Annotated Conversational Dialog Dataset for Grammatical Error Correction | https://aclanthology.org/2022.naacl-main.5/ | [
"Xun Yuan",
"Derek Pham",
"Sam Davidson",
"Zhou Yu"
] | Currently available grammatical error correction (GEC) datasets are compiled using essays or other long-form text written by language learners, limiting the applicability of these datasets to other domains such as informal writing and conversational dialog. In this paper, we present a novel GEC dataset consisting of pa... | 2022.naacl-main.5 | 10.18653/v1/2022.naacl-main.5 | null | 2112.08466 | title_snapshot |
2022.naacl-main.6 | Semantic Diversity in Dialogue with Natural Language Inference | https://aclanthology.org/2022.naacl-main.6/ | [
"Katherine Stasaski",
"Marti Hearst"
] | Generating diverse, interesting responses to chitchat conversations is a problem for neural conversational agents. This paper makes two substantial contributions to improving diversity in dialogue generation. First, we propose a novel metric which uses Natural Language Inference (NLI) to measure the semantic diversity ... | 2022.naacl-main.6 | 10.18653/v1/2022.naacl-main.6 | null | 2205.01497 | title_snapshot |
2022.naacl-main.7 | LEA: Meta Knowledge-Driven Self-Attentive Document Embedding for Few-Shot Text Classification | https://aclanthology.org/2022.naacl-main.7/ | [
"S. K. Hong",
"Tae Young Jang"
] | Text classification has achieved great success with the prosperity of deep learning and pre-trained language models. However, we often encounter labeled data deficiency problems in real-world text-classification tasks. To overcome such challenging scenarios, interest in few-shot learning has increased, whereas most few... | 2022.naacl-main.7 | 10.18653/v1/2022.naacl-main.7 | null | null | null |
2022.naacl-main.8 | Enhancing Self-Attention with Knowledge-Assisted Attention Maps | https://aclanthology.org/2022.naacl-main.8/ | [
"Jiangang Bai",
"Yujing Wang",
"Hong Sun",
"Ruonan Wu",
"Tianmeng Yang",
"Pengfei Tang",
"Defu Cao",
"Mingliang Zhang",
"Yunhai Tong",
"Yaming Yang",
"Jing Bai",
"Ruofei Zhang",
"Hao Sun",
"Wei Shen"
] | Large-scale pre-trained language models have attracted extensive attentions in the research community and shown promising results on various tasks of natural language processing. However, the attention maps, which record the attention scores between tokens in self-attention mechanism, are sometimes ineffective as they ... | 2022.naacl-main.8 | 10.18653/v1/2022.naacl-main.8 | null | null | null |
2022.naacl-main.9 | Batch-Softmax Contrastive Loss for Pairwise Sentence Scoring Tasks | https://aclanthology.org/2022.naacl-main.9/ | [
"Anton Chernyavskiy",
"Dmitry Ilvovsky",
"Pavel Kalinin",
"Preslav Nakov"
] | The use of contrastive loss for representation learning has become prominent in computer vision, and it is now getting attention in Natural Language Processing (NLP).Here, we explore the idea of using a batch-softmax contrastive loss when fine-tuning large-scale pre-trained transformer models to learn better task-speci... | 2022.naacl-main.9 | 10.18653/v1/2022.naacl-main.9 | null | 2110.15725 | title_snapshot |
2022.naacl-main.10 | NewsEdits: A News Article Revision Dataset and a Novel Document-Level Reasoning Challenge | https://aclanthology.org/2022.naacl-main.10/ | [
"Alexander Spangher",
"Xiang Ren",
"Jonathan May",
"Nanyun Peng"
] | News article revision histories provide clues to narrative and factual evolution in news articles. To facilitate analysis of this evolution, we present the first publicly available dataset of news revision histories, NewsEdits. Our dataset is large-scale and multilingual; it contains 1.2 million articles with 4.6 milli... | 2022.naacl-main.10 | 10.18653/v1/2022.naacl-main.10 | Honorable mention for contributions to resources | 2206.07106 | title_judge |
2022.naacl-main.11 | Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia | https://aclanthology.org/2022.naacl-main.11/ | [
"Samee Ibraheem",
"Gaoyue Zhou",
"John DeNero"
] | While neural networks demonstrate a remarkable ability to model linguistic content, capturing contextual information related to a speaker’s conversational role is an open area of research. In this work, we analyze the effect of speaker role on language use through the game of Mafia, in which participants are assigned e... | 2022.naacl-main.11 | 10.18653/v1/2022.naacl-main.11 | null | 2207.02253 | title_snapshot |
2022.naacl-main.12 | SUBS: Subtree Substitution for Compositional Semantic Parsing | https://aclanthology.org/2022.naacl-main.12/ | [
"Jingfeng Yang",
"Le Zhang",
"Diyi Yang"
] | Although sequence-to-sequence models often achieve good performance in semantic parsing for i.i.d. data, their performance is still inferior in compositional generalization. Several data augmentation methods have been proposed to alleviate this problem. However, prior work only leveraged superficial grammar or rules fo... | 2022.naacl-main.12 | 10.18653/v1/2022.naacl-main.12 | null | 2205.01538 | title_snapshot |
2022.naacl-main.13 | Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks | https://aclanthology.org/2022.naacl-main.13/ | [
"Paul Röttger",
"Bertie Vidgen",
"Dirk Hovy",
"Janet Pierrehumbert"
] | Labelled data is the foundation of most natural language processing tasks. However, labelling data is difficult and there often are diverse valid beliefs about what the correct data labels should be. So far, dataset creators have acknowledged annotator subjectivity, but rarely actively managed it in the annotation proc... | 2022.naacl-main.13 | 10.18653/v1/2022.naacl-main.13 | null | 2112.07475 | title_snapshot |
2022.naacl-main.14 | Do Deep Neural Nets Display Human-like Attention in Short Answer Scoring? | https://aclanthology.org/2022.naacl-main.14/ | [
"Zijie Zeng",
"Xinyu Li",
"Dragan Gasevic",
"Guanliang Chen"
] | Deep Learning (DL) techniques have been increasingly adopted for Automatic Text Scoring in education. However, these techniques often suffer from their inabilities to explain and justify how a prediction is made, which, unavoidably, decreases their trustworthiness and hinders educators from embracing them in practice. ... | 2022.naacl-main.14 | 10.18653/v1/2022.naacl-main.14 | null | null | null |
2022.naacl-main.15 | Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation | https://aclanthology.org/2022.naacl-main.15/ | [
"Yu Li",
"Baolin Peng",
"Yelong Shen",
"Yi Mao",
"Lars Liden",
"Zhou Yu",
"Jianfeng Gao"
] | Knowledge-grounded dialogue systems are challenging to build due to the lack of training data and heterogeneous knowledge sources. Existing systems perform poorly on unseen topics due to limited topics covered in the training data. In addition, it is challenging to generalize to the domains that require different types... | 2022.naacl-main.15 | 10.18653/v1/2022.naacl-main.15 | null | 2112.07924 | title_snapshot |
2022.naacl-main.16 | CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data | https://aclanthology.org/2022.naacl-main.16/ | [
"Rui Feng",
"Chen Luo",
"Qingyu Yin",
"Bing Yin",
"Tuo Zhao",
"Chao Zhang"
] | User sessions empower many search and recommendation tasks on a daily basis. Such session data are semi-structured, which encode heterogeneous relations between queries and products, and each item is described by the unstructured text. Despite recent advances in self-supervised learning for text or graphs, there lack o... | 2022.naacl-main.16 | 10.18653/v1/2022.naacl-main.16 | null | 2204.04303 | title_snapshot |
2022.naacl-main.17 | Political Ideology and Polarization: A Multi-dimensional Approach | https://aclanthology.org/2022.naacl-main.17/ | [
"Barea Sinno",
"Bernardo Oviedo",
"Katherine Atwell",
"Malihe Alikhani",
"Junyi Jessy Li"
] | Analyzing ideology and polarization is of critical importance in advancing our grasp of modern politics. Recent research has made great strides towards understanding the ideological bias (i.e., stance) of news media along the left-right spectrum. In this work, we instead take a novel and more nuanced approach for the s... | 2022.naacl-main.17 | 10.18653/v1/2022.naacl-main.17 | null | null | null |
2022.naacl-main.18 | Cooperative Self-training of Machine Reading Comprehension | https://aclanthology.org/2022.naacl-main.18/ | [
"Hongyin Luo",
"Shang-Wen Li",
"Mingye Gao",
"Seunghak Yu",
"James Glass"
] | Pretrained language models have significantly improved the performance of downstream language understanding tasks, including extractive question answering, by providing high-quality contextualized word embeddings. However, training question answering models still requires large amounts of annotated data for specific do... | 2022.naacl-main.18 | 10.18653/v1/2022.naacl-main.18 | null | 2103.07449 | title_snapshot |
2022.naacl-main.19 | GlobEnc: Quantifying Global Token Attribution by Incorporating the Whole Encoder Layer in Transformers | https://aclanthology.org/2022.naacl-main.19/ | [
"Ali Modarressi",
"Mohsen Fayyaz",
"Yadollah Yaghoobzadeh",
"Mohammad Taher Pilehvar"
] | There has been a growing interest in interpreting the underlying dynamics of Transformers. While self-attention patterns were initially deemed as the primary option, recent studies have shown that integrating other components can yield more accurate explanations. This paper introduces a novel token attribution analysis... | 2022.naacl-main.19 | 10.18653/v1/2022.naacl-main.19 | null | 2205.03286 | title_snapshot |
2022.naacl-main.20 | A Robustly Optimized BMRC for Aspect Sentiment Triplet Extraction | https://aclanthology.org/2022.naacl-main.20/ | [
"Shu Liu",
"Kaiwen Li",
"Zuhe Li"
] | Aspect sentiment triplet extraction (ASTE) is a challenging subtask in aspect-based sentiment analysis. It aims to explore the triplets of aspects, opinions and sentiments with complex correspondence from the context. The bidirectional machine reading comprehension (BMRC), can effectively deal with ASTE task, but sever... | 2022.naacl-main.20 | 10.18653/v1/2022.naacl-main.20 | null | null | null |
2022.naacl-main.21 | Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds | https://aclanthology.org/2022.naacl-main.21/ | [
"Yu Zhang",
"Yu Meng",
"Xuan Wang",
"Sheng Wang",
"Jiawei Han"
] | Discovering latent topics from text corpora has been studied for decades. Many existing topic models adopt a fully unsupervised setting, and their discovered topics may not cater to users’ particular interests due to their inability of leveraging user guidance. Although there exist seed-guided topic discovery approache... | 2022.naacl-main.21 | 10.18653/v1/2022.naacl-main.21 | null | 2205.01845 | title_snapshot |
2022.naacl-main.22 | Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needs | https://aclanthology.org/2022.naacl-main.22/ | [
"Xu Wang",
"Simin Fan",
"Jessica Houghton",
"Lu Wang"
] | NLP-powered automatic question generation (QG) techniques carry great pedagogical potential of saving educators’ time and benefiting student learning. Yet, QG systems have not been widely adopted in classrooms to date. In this work, we aim to pinpoint key impediments and investigate how to improve the usability of auto... | 2022.naacl-main.22 | 10.18653/v1/2022.naacl-main.22 | null | 2205.00355 | title_snapshot |
2022.naacl-main.23 | SwahBERT: Language Model of Swahili | https://aclanthology.org/2022.naacl-main.23/ | [
"Gati Martin",
"Medard Edmund Mswahili",
"Young-Seob Jeong",
"Jiyoung Woo"
] | The rapid development of social networks, electronic commerce, mobile Internet, and other technologies, has influenced the growth of Web data. Social media and Internet forums are valuable sources of citizens’ opinions, which can be analyzed for community development and user behavior analysis. Unfortunately, the scarc... | 2022.naacl-main.23 | 10.18653/v1/2022.naacl-main.23 | null | null | null |
2022.naacl-main.24 | Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications | https://aclanthology.org/2022.naacl-main.24/ | [
"Kaitlyn Zhou",
"Su Lin Blodgett",
"Adam Trischler",
"Hal Daumé III",
"Kaheer Suleman",
"Alexandra Olteanu"
] | There are many ways to express similar things in text, which makes evaluating natural language generation (NLG) systems difficult. Compounding this difficulty is the need to assess varying quality criteria depending on the deployment setting. While the landscape of NLG evaluation has been well-mapped, practitioners’ go... | 2022.naacl-main.24 | 10.18653/v1/2022.naacl-main.24 | null | 2205.06828 | title_snapshot |
2022.naacl-main.25 | TSTR: Too Short to Represent, Summarize with Details! Intro-Guided Extended Summary Generation | https://aclanthology.org/2022.naacl-main.25/ | [
"Sajad Sotudeh",
"Nazli Goharian"
] | Many scientific papers such as those in arXiv and PubMed data collections have abstracts with varying lengths of 50-1000 words and average length of approximately 200 words, where longer abstracts typically convey more information about the source paper. Up to recently, scientific summarization research has typically f... | 2022.naacl-main.25 | 10.18653/v1/2022.naacl-main.25 | null | 2206.00847 | title_snapshot |
2022.naacl-main.26 | Empathic Machines: Using Intermediate Features as Levers to Emulate Emotions in Text-To-Speech Systems | https://aclanthology.org/2022.naacl-main.26/ | [
"Saiteja Kosgi",
"Sarath Sivaprasad",
"Niranjan Pedanekar",
"Anil Nelakanti",
"Vineet Gandhi"
] | We present a method to control the emotional prosody of Text to Speech (TTS) systems by using phoneme-level intermediate features (pitch, energy, and duration) as levers. As a key idea, we propose Differential Scaling (DS) to disentangle features relating to affective prosody from those arising due to acoustics conditi... | 2022.naacl-main.26 | 10.18653/v1/2022.naacl-main.26 | null | null | null |
2022.naacl-main.27 | The Why and The How: A Survey on Natural Language Interaction in Visualization | https://aclanthology.org/2022.naacl-main.27/ | [
"Henrik Voigt",
"Ozge Alacam",
"Monique Meuschke",
"Kai Lawonn",
"Sina Zarrieß"
] | Natural language as a modality of interaction is becoming increasingly popular in the field of visualization. In addition to the popular query interfaces, other language-based interactions such as annotations, recommendations, explanations, or documentation experience growing interest. In this survey, we provide an ove... | 2022.naacl-main.27 | 10.18653/v1/2022.naacl-main.27 | null | null | null |
2022.naacl-main.28 | Understand before Answer: Improve Temporal Reading Comprehension via Precise Question Understanding | https://aclanthology.org/2022.naacl-main.28/ | [
"Hao Huang",
"Xiubo Geng",
"Guodong Long",
"Daxin Jiang"
] | This work studies temporal reading comprehension (TRC), which reads a free-text passage and answers temporal ordering questions. Precise question understanding is critical for temporal reading comprehension. For example, the question “What happened before the victory” and “What happened after the victory” share almost ... | 2022.naacl-main.28 | 10.18653/v1/2022.naacl-main.28 | null | null | null |
2022.naacl-main.29 | User-Driven Research of Medical Note Generation Software | https://aclanthology.org/2022.naacl-main.29/ | [
"Tom Knoll",
"Francesco Moramarco",
"Alex Papadopoulos Korfiatis",
"Rachel Young",
"Claudia Ruffini",
"Mark Perera",
"Christian Perstl",
"Ehud Reiter",
"Anya Belz",
"Aleksandar Savkov"
] | A growing body of work uses Natural Language Processing (NLP) methods to automatically generate medical notes from audio recordings of doctor-patient consultations. However, there are very few studies on how such systems could be used in clinical practice, how clinicians would adjust to using them, or how system design... | 2022.naacl-main.29 | 10.18653/v1/2022.naacl-main.29 | Best paper on human-centered NLP special theme | 2205.02549 | title_snapshot |
2022.naacl-main.30 | Ask Me Anything in Your Native Language | https://aclanthology.org/2022.naacl-main.30/ | [
"Nikita Sorokin",
"Dmitry Abulkhanov",
"Irina Piontkovskaya",
"Valentin Malykh"
] | Cross-lingual question answering is a thriving field in the modern world, helping people to search information on the web more efficiently. One of the important scenarios is to give an answer even there is no answer in the language a person asks a question with. We present a novel approach based on single encoder for q... | 2022.naacl-main.30 | 10.18653/v1/2022.naacl-main.30 | null | null | null |
2022.naacl-main.31 | Diversifying Neural Dialogue Generation via Negative Distillation | https://aclanthology.org/2022.naacl-main.31/ | [
"Yiwei Li",
"Shaoxiong Feng",
"Bin Sun",
"Kan Li"
] | Generative dialogue models suffer badly from the generic response problem, limiting their applications to a few toy scenarios. Recently, an interesting approach, namely negative training, has been proposed to alleviate this problem by reminding the model not to generate high-frequency responses during training. However... | 2022.naacl-main.31 | 10.18653/v1/2022.naacl-main.31 | null | 2205.02795 | title_snapshot |
2022.naacl-main.32 | On Synthetic Data for Back Translation | https://aclanthology.org/2022.naacl-main.32/ | [
"Jiahao Xu",
"Yubin Ruan",
"Wei Bi",
"Guoping Huang",
"Shuming Shi",
"Lihui Chen",
"Lemao Liu"
] | Back translation (BT) is one of the most significant technologies in NMT research fields. Existing attempts on BT share a common characteristic: they employ either beam search or random sampling to generate synthetic data with a backward model but seldom work studies the role of synthetic data in the performance of BT.... | 2022.naacl-main.32 | 10.18653/v1/2022.naacl-main.32 | null | 2310.13675 | title_snapshot |
2022.naacl-main.33 | Mapping the Design Space of Human-AI Interaction in Text Summarization | https://aclanthology.org/2022.naacl-main.33/ | [
"Ruijia Cheng",
"Alison Smith-Renner",
"Ke Zhang",
"Joel Tetreault",
"Alejandro Jaimes-Larrarte"
] | Automatic text summarization systems commonly involve humans for preparing data or evaluating model performance, yet, there lacks a systematic understanding of humans’ roles, experience, and needs when interacting with or being assisted by AI. From a human-centered perspective, we map the design opportunities and consi... | 2022.naacl-main.33 | 10.18653/v1/2022.naacl-main.33 | null | 2206.14863 | title_snapshot |
2022.naacl-main.34 | Towards Robust and Semantically Organised Latent Representations for Unsupervised Text Style Transfer | https://aclanthology.org/2022.naacl-main.34/ | [
"Sharan Narasimhan",
"Suvodip Dey",
"Maunendra Desarkar"
] | Recent studies show that auto-encoder based approaches successfully perform language generation, smooth sentence interpolation, and style transfer over unseen attributes using unlabelled datasets in a zero-shot manner. The latent space geometry of such models is organised well enough to perform on datasets where the st... | 2022.naacl-main.34 | 10.18653/v1/2022.naacl-main.34 | null | 2205.02309 | title_snapshot |
2022.naacl-main.35 | An Exploration of Post-Editing Effectiveness in Text Summarization | https://aclanthology.org/2022.naacl-main.35/ | [
"Vivian Lai",
"Alison Smith-Renner",
"Ke Zhang",
"Ruijia Cheng",
"Wenjuan Zhang",
"Joel Tetreault",
"Alejandro Jaimes-Larrarte"
] | Automatic summarization methods are efficient but can suffer from low quality. In comparison, manual summarization is expensive but produces higher quality. Can humans and AI collaborate to improve summarization performance? In similar text generation tasks (e.g., machine translation), human-AI collaboration in the for... | 2022.naacl-main.35 | 10.18653/v1/2022.naacl-main.35 | null | 2206.06383 | title_snapshot |
2022.naacl-main.36 | Automatic Correction of Human Translations | https://aclanthology.org/2022.naacl-main.36/ | [
"Jessy Lin",
"Geza Kovacs",
"Aditya Shastry",
"Joern Wuebker",
"John DeNero"
] | We introduce translation error correction (TEC), the task of automatically correcting human-generated translations. Imperfections in machine translations (MT) have long motivated systems for improving translations post-hoc with automatic post-editing. In contrast, little attention has been devoted to the problem of aut... | 2022.naacl-main.36 | 10.18653/v1/2022.naacl-main.36 | Best new task (tied) and new resource paper | 2206.08593 | title_snapshot |
2022.naacl-main.37 | On the Robustness of Reading Comprehension Models to Entity Renaming | https://aclanthology.org/2022.naacl-main.37/ | [
"Jun Yan",
"Yang Xiao",
"Sagnik Mukherjee",
"Bill Yuchen Lin",
"Robin Jia",
"Xiang Ren"
] | We study the robustness of machine reading comprehension (MRC) models to entity renaming—do models make more wrong predictions when the same questions are asked about an entity whose name has been changed? Such failures imply that models overly rely on entity information to answer questions, and thus may generalize poo... | 2022.naacl-main.37 | 10.18653/v1/2022.naacl-main.37 | null | 2110.08555 | title_snapshot |
2022.naacl-main.38 | Explaining Why: How Instructions and User Interfaces Impact Annotator Rationales When Labeling Text Data | https://aclanthology.org/2022.naacl-main.38/ | [
"Jamar Sullivan Jr.",
"Will Brackenbury",
"Andrew McNutt",
"Kevin Bryson",
"Kwam Byll",
"Yuxin Chen",
"Michael Littman",
"Chenhao Tan",
"Blase Ur"
] | In the context of data labeling, NLP researchers are increasingly interested in having humans select rationales, a subset of input tokens relevant to the chosen label. We conducted a 332-participant online user study to understand how humans select rationales, especially how different instructions and user interface af... | 2022.naacl-main.38 | 10.18653/v1/2022.naacl-main.38 | null | null | null |
2022.naacl-main.39 | Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization | https://aclanthology.org/2022.naacl-main.39/ | [
"Haode Zhang",
"Haowen Liang",
"Yuwei Zhang",
"Liming Zhan",
"Xiaolei Lu",
"Albert Lam",
"Xiao-Ming Wu"
] | It is challenging to train a good intent classifier for a task-oriented dialogue system with only a few annotations. Recent studies have shown that fine-tuning pre-trained language models with a small set of labeled utterances from public benchmarks in a supervised manner is extremely helpful. However, we find that sup... | 2022.naacl-main.39 | 10.18653/v1/2022.naacl-main.39 | null | 2205.07208 | title_snapshot |
2022.naacl-main.40 | Cross-document Misinformation Detection based on Event Graph Reasoning | https://aclanthology.org/2022.naacl-main.40/ | [
"Xueqing Wu",
"Kung-Hsiang Huang",
"Yi Fung",
"Heng Ji"
] | For emerging events, human readers are often exposed to both real news and fake news. Multiple news articles may contain complementary or contradictory information that readers can leverage to help detect fake news. Inspired by this process, we propose a novel task of cross-document misinformation detection. Given a cl... | 2022.naacl-main.40 | 10.18653/v1/2022.naacl-main.40 | null | null | null |
2022.naacl-main.41 | Disentangled Action Recognition with Knowledge Bases | https://aclanthology.org/2022.naacl-main.41/ | [
"Zhekun Luo",
"Shalini Ghosh",
"Devin Guillory",
"Keizo Kato",
"Trevor Darrell",
"Huijuan Xu"
] | Action in video usually involves the interaction of human with objects. Action labels are typically composed of various combinations of verbs and nouns, but we may not have training data for all possible combinations. In this paper, we aim to improve the generalization ability of the compositional action recognition mo... | 2022.naacl-main.41 | 10.18653/v1/2022.naacl-main.41 | null | 2207.01708 | title_snapshot |
2022.naacl-main.42 | Machine-in-the-Loop Rewriting for Creative Image Captioning | https://aclanthology.org/2022.naacl-main.42/ | [
"Vishakh Padmakumar",
"He He"
] | Machine-in-the-loop writing aims to build models that assist humans to accomplish their writing tasks more effectively. Prior work has found that providing users a machine-written draft or sentence-level continuations has limited success since the generated text tends to deviate from users’ intention. To allow the user... | 2022.naacl-main.42 | 10.18653/v1/2022.naacl-main.42 | null | 2111.04193 | title_snapshot |
2022.naacl-main.43 | A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction | https://aclanthology.org/2022.naacl-main.43/ | [
"Yong Xie",
"Dakuo Wang",
"Pin-Yu Chen",
"Jinjun Xiong",
"Sijia Liu",
"Oluwasanmi Koyejo"
] | More and more investors and machine learning models rely on social media (e.g., Twitter and Reddit) to gather information and predict movements stock prices. Although text-based models are known to be vulnerable to adversarial attacks, whether stock prediction models have similar vulnerability given necessary constrain... | 2022.naacl-main.43 | 10.18653/v1/2022.naacl-main.43 | null | 2205.01094 | title_judge |
2022.naacl-main.44 | Building Multilingual Machine Translation Systems That Serve Arbitrary XY Translations | https://aclanthology.org/2022.naacl-main.44/ | [
"Akiko Eriguchi",
"Shufang Xie",
"Tao Qin",
"Hany Hassan"
] | Multilingual Neural Machine Translation (MNMT) enables one system to translate sentences from multiple source languages to multiple target languages, greatly reducing deployment costs compared with conventional bilingual systems. The MNMT training benefit, however, is often limited to many-to-one directions. The model ... | 2022.naacl-main.44 | 10.18653/v1/2022.naacl-main.44 | null | 2206.14982 | title_judge |
2022.naacl-main.45 | Non-Autoregressive Neural Machine Translation with Consistency Regularization Optimized Variational Framework | https://aclanthology.org/2022.naacl-main.45/ | [
"Minghao Zhu",
"Junli Wang",
"Chungang Yan"
] | Variational Autoencoder (VAE) is an effective framework to model the interdependency for non-autoregressive neural machine translation (NAT). One of the prominent VAE-based NAT frameworks, LaNMT, achieves great improvements to vanilla models, but still suffers from two main issues which lower down the translation quali... | 2022.naacl-main.45 | 10.18653/v1/2022.naacl-main.45 | null | null | null |
2022.naacl-main.46 | User-Centric Gender Rewriting | https://aclanthology.org/2022.naacl-main.46/ | [
"Bashar Alhafni",
"Nizar Habash",
"Houda Bouamor"
] | In this paper, we define the task of gender rewriting in contexts involving two users (I and/or You) – first and second grammatical persons with independent grammatical gender preferences. We focus on Arabic, a gender-marking morphologically rich language. We develop a multi-step system that combines the positive aspec... | 2022.naacl-main.46 | 10.18653/v1/2022.naacl-main.46 | null | 2205.02211 | title_snapshot |
2022.naacl-main.47 | Reframing Human-AI Collaboration for Generating Free-Text Explanations | https://aclanthology.org/2022.naacl-main.47/ | [
"Sarah Wiegreffe",
"Jack Hessel",
"Swabha Swayamdipta",
"Mark Riedl",
"Yejin Choi"
] | Large language models are increasingly capable of generating fluent-appearing text with relatively little task-specific supervision. But can these models accurately explain classification decisions? We consider the task of generating free-text explanations using human-written examples in a few-shot manner. We find that... | 2022.naacl-main.47 | 10.18653/v1/2022.naacl-main.47 | null | 2112.08674 | title_snapshot |
2022.naacl-main.48 | EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction | https://aclanthology.org/2022.naacl-main.48/ | [
"Benfeng Xu",
"Quan Wang",
"Yajuan Lyu",
"Yabing Shi",
"Yong Zhu",
"Jie Gao",
"Zhendong Mao"
] | Multi-triple extraction is a challenging task due to the existence of informative inter-triple correlations, and consequently rich interactions across the constituent entities and relations. While existing works only explore entity representations, we propose to explicitly introduce relation representation, jointly rep... | 2022.naacl-main.48 | 10.18653/v1/2022.naacl-main.48 | null | null | null |
2022.naacl-main.49 | Meta Learning for Natural Language Processing: A Survey | https://aclanthology.org/2022.naacl-main.49/ | [
"Hung-yi Lee",
"Shang-Wen Li",
"Thang Vu"
] | Deep learning has been the mainstream technique in the natural language processing (NLP) area. However, deep learning requires many labeled data and is less generalizable across domains. Meta-learning is an arising field in machine learning. It studies approaches to learning better learning algorithms and aims to impro... | 2022.naacl-main.49 | 10.18653/v1/2022.naacl-main.49 | null | 2205.01500 | title_snapshot |
2022.naacl-main.50 | Analyzing Modality Robustness in Multimodal Sentiment Analysis | https://aclanthology.org/2022.naacl-main.50/ | [
"Devamanyu Hazarika",
"Yingting Li",
"Bo Cheng",
"Shuai Zhao",
"Roger Zimmermann",
"Soujanya Poria"
] | Building robust multimodal models are crucial for achieving reliable deployment in the wild. Despite its importance, less attention has been paid to identifying and improving the robustness of Multimodal Sentiment Analysis (MSA) models. In this work, we hope to address that by (i) Proposing simple diagnostic checks for... | 2022.naacl-main.50 | 10.18653/v1/2022.naacl-main.50 | null | 2205.15465 | title_snapshot |
2022.naacl-main.51 | Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text Generation | https://aclanthology.org/2022.naacl-main.51/ | [
"Jinyi Hu",
"Xiaoyuan Yi",
"Wenhao Li",
"Maosong Sun",
"Xing Xie"
] | The past several years have witnessed Variational Auto-Encoder’s superiority in various text generation tasks. However, due to the sequential nature of the text, auto-regressive decoders tend to ignore latent variables and then reduce to simple language models, known as the \textit{KL vanishing} problem, which would fu... | 2022.naacl-main.51 | 10.18653/v1/2022.naacl-main.51 | null | 2207.06130 | title_snapshot |
2022.naacl-main.52 | Easy Adaptation to Mitigate Gender Bias in Multilingual Text Classification | https://aclanthology.org/2022.naacl-main.52/ | [
"Xiaolei Huang"
] | Existing approaches to mitigate demographic biases evaluate on monolingual data, however, multilingual data has not been examined. In this work, we treat the gender as domains (e.g., male vs. female) and present a standard domain adaptation model to reduce the gender bias and improve performance of text classifiers und... | 2022.naacl-main.52 | 10.18653/v1/2022.naacl-main.52 | null | 2204.05459 | title_snapshot |
2022.naacl-main.53 | On the Use of External Data for Spoken Named Entity Recognition | https://aclanthology.org/2022.naacl-main.53/ | [
"Ankita Pasad",
"Felix Wu",
"Suwon Shon",
"Karen Livescu",
"Kyu Han"
] | Spoken language understanding (SLU) tasks involve mapping from speech signals to semantic labels. Given the complexity of such tasks, good performance is expected to require large labeled datasets, which are difficult to collect for each new task and domain. However, recent advances in self-supervised speech representa... | 2022.naacl-main.53 | 10.18653/v1/2022.naacl-main.53 | null | 2112.07648 | title_snapshot |
2022.naacl-main.54 | Long-term Control for Dialogue Generation: Methods and Evaluation | https://aclanthology.org/2022.naacl-main.54/ | [
"Ramya Ramakrishnan",
"Hashan Narangodage",
"Mauro Schilman",
"Kilian Weinberger",
"Ryan McDonald"
] | Current approaches for controlling dialogue response generation are primarily focused on high-level attributes like style, sentiment, or topic. In this work, we focus on constrained long-term dialogue generation, which involves more fine-grained control and requires a given set of control words to appear in generated r... | 2022.naacl-main.54 | 10.18653/v1/2022.naacl-main.54 | null | 2205.07352 | title_snapshot |
2022.naacl-main.55 | Learning Dialogue Representations from Consecutive Utterances | https://aclanthology.org/2022.naacl-main.55/ | [
"Zhihan Zhou",
"Dejiao Zhang",
"Wei Xiao",
"Nicholas Dingwall",
"Xiaofei Ma",
"Andrew Arnold",
"Bing Xiang"
] | Learning high-quality dialogue representations is essential for solving a variety of dialogue-oriented tasks, especially considering that dialogue systems often suffer from data scarcity. In this paper, we introduce Dialogue Sentence Embedding (DSE), a self-supervised contrastive learning method that learns effective d... | 2022.naacl-main.55 | 10.18653/v1/2022.naacl-main.55 | null | 2205.13568 | title_snapshot |
2022.naacl-main.56 | On the Machine Learning of Ethical Judgments from Natural Language | https://aclanthology.org/2022.naacl-main.56/ | [
"Zeerak Talat",
"Hagen Blix",
"Josef Valvoda",
"Maya Indira Ganesh",
"Ryan Cotterell",
"Adina Williams"
] | Ethics is one of the longest standing intellectual endeavors of humanity. In recent years, the fields of AI and NLP have attempted to address issues of harmful outcomes in machine learning systems that are made to interface with humans. One recent approach in this vein is the construction of NLP morality models that ca... | 2022.naacl-main.56 | 10.18653/v1/2022.naacl-main.56 | null | null | null |
2022.naacl-main.57 | NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics | https://aclanthology.org/2022.naacl-main.57/ | [
"Ximing Lu",
"Sean Welleck",
"Peter West",
"Liwei Jiang",
"Jungo Kasai",
"Daniel Khashabi",
"Ronan Le Bras",
"Lianhui Qin",
"Youngjae Yu",
"Rowan Zellers",
"Noah A. Smith",
"Yejin Choi"
] | The dominant paradigm for neural text generation is left-to-right decoding from autoregressive language models. Constrained or controllable generation under complex lexical constraints, however, requires foresight to plan ahead feasible future paths. Drawing inspiration from the A^* search algorithm, we propose NeuroLo... | 2022.naacl-main.57 | 10.18653/v1/2022.naacl-main.57 | Best new method paper | 2112.08726 | title_snapshot |
2022.naacl-main.58 | PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining | https://aclanthology.org/2022.naacl-main.58/ | [
"Machel Reid",
"Mikel Artetxe"
] | Despite the success of multilingual sequence-to-sequence pretraining, most existing approaches rely on monolingual corpora and do not make use of the strong cross-lingual signal contained in parallel data. In this paper, we present PARADISE (PARAllel &Denoising Integration in SEquence-to-sequence models), which extends... | 2022.naacl-main.58 | 10.18653/v1/2022.naacl-main.58 | null | 2108.01887 | title_snapshot |
2022.naacl-main.59 | Explaining Toxic Text via Knowledge Enhanced Text Generation | https://aclanthology.org/2022.naacl-main.59/ | [
"Rohit Sridhar",
"Diyi Yang"
] | Warning: This paper contains content that is offensive and may be upsetting. Biased or toxic speech can be harmful to various demographic groups. Therefore, it is not only important for models to detect these speech, but to also output explanations of why a given text is toxic. Previous literature has mostly focused on... | 2022.naacl-main.59 | 10.18653/v1/2022.naacl-main.59 | null | null | null |
2022.naacl-main.60 | Teaching BERT to Wait: Balancing Accuracy and Latency for Streaming Disfluency Detection | https://aclanthology.org/2022.naacl-main.60/ | [
"Angelica Chen",
"Vicky Zayats",
"Daniel Walker",
"Dirk Padfield"
] | In modern interactive speech-based systems, speech is consumed and transcribed incrementally prior to having disfluencies removed. While this post-processing step is crucial for producing clean transcripts and high performance on downstream tasks (e.g. machine translation), most current state-of-the-art NLP models such... | 2022.naacl-main.60 | 10.18653/v1/2022.naacl-main.60 | null | 2205.00620 | title_snapshot |
2022.naacl-main.61 | GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filtering | https://aclanthology.org/2022.naacl-main.61/ | [
"Yoonseok Yang",
"Kyu Seok Kim",
"Minsam Kim",
"Juneyoung Park"
] | Content-based collaborative filtering (CCF) predicts user-item interactions based on both users’ interaction history and items’ content information. Recently, pre-trained language models (PLM) have been used to extract high-quality item encodings for CCF. However, it is resource-intensive to train a PLM-based CCF model... | 2022.naacl-main.61 | 10.18653/v1/2022.naacl-main.61 | null | 2204.04179 | title_snapshot |
2022.naacl-main.62 | Generating Repetitions with Appropriate Repeated Words | https://aclanthology.org/2022.naacl-main.62/ | [
"Toshiki Kawamoto",
"Hidetaka Kamigaito",
"Kotaro Funakoshi",
"Manabu Okumura"
] | A repetition is a response that repeats words in the previous speaker’s utterance in a dialogue. Repetitions are essential in communication to build trust with others, as investigated in linguistic studies. In this work, we focus on repetition generation. To the best of our knowledge, this is the first neural approach ... | 2022.naacl-main.62 | 10.18653/v1/2022.naacl-main.62 | null | 2207.00929 | title_snapshot |
2022.naacl-main.63 | Textless Speech-to-Speech Translation on Real Data | https://aclanthology.org/2022.naacl-main.63/ | [
"Ann Lee",
"Hongyu Gong",
"Paul-Ambroise Duquenne",
"Holger Schwenk",
"Peng-Jen Chen",
"Changhan Wang",
"Sravya Popuri",
"Yossi Adi",
"Juan Pino",
"Jiatao Gu",
"Wei-Ning Hsu"
] | We present a textless speech-to-speech translation (S2ST) system that can translate speech from one language into another language and can be built without the need of any text data. Different from existing work in the literature, we tackle the challenge in modeling multi-speaker target speech and train the systems wit... | 2022.naacl-main.63 | 10.18653/v1/2022.naacl-main.63 | null | 2112.08352 | title_snapshot |
2022.naacl-main.64 | WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding | https://aclanthology.org/2022.naacl-main.64/ | [
"Guoqing Zheng",
"Giannis Karamanolakis",
"Kai Shu",
"Ahmed Awadallah"
] | Building machine learning models for natural language understanding (NLU) tasks relies heavily on labeled data. Weak supervision has been proven valuable when large amount of labeled data is unavailable or expensive to obtain. Existing works studying weak supervision for NLU either mostly focus on a specific task or si... | 2022.naacl-main.64 | 10.18653/v1/2022.naacl-main.64 | null | 2108.12603 | title_snapshot |
2022.naacl-main.65 | CompactIE: Compact Facts in Open Information Extraction | https://aclanthology.org/2022.naacl-main.65/ | [
"Farima Fatahi Bayat",
"Nikita Bhutani",
"H. Jagadish"
] | A major drawback of modern neural OpenIE systems and benchmarks is that they prioritize high coverage of information in extractions over compactness of their constituents. This severely limits the usefulness of OpenIE extractions in many downstream tasks. The utility of extractions can be improved if extractions are co... | 2022.naacl-main.65 | 10.18653/v1/2022.naacl-main.65 | null | 2205.02880 | title_snapshot |
2022.naacl-main.66 | CoSIm: Commonsense Reasoning for Counterfactual Scene Imagination | https://aclanthology.org/2022.naacl-main.66/ | [
"Hyounghun Kim",
"Abhay Zala",
"Mohit Bansal"
] | As humans, we can modify our assumptions about a scene by imagining alternative objects or concepts in our minds. For example, we can easily anticipate the implications of the sun being overcast by rain clouds (e.g., the street will get wet) and accordingly prepare for that. In this paper, we introduce a new dataset ca... | 2022.naacl-main.66 | 10.18653/v1/2022.naacl-main.66 | null | 2207.03961 | title_snapshot |
2022.naacl-main.67 | Abstraction not Memory: BERT and the English Article System | https://aclanthology.org/2022.naacl-main.67/ | [
"Harish Tayyar Madabushi",
"Dagmar Divjak",
"Petar Milin"
] | Article prediction is a task that has long defied accurate linguistic description. As such, this task is ideally suited to evaluate models on their ability to emulate native-speaker intuition. To this end, we compare the performance of native English speakers and pre-trained models on the task of article prediction set... | 2022.naacl-main.67 | 10.18653/v1/2022.naacl-main.67 | null | 2206.04184 | title_snapshot |
2022.naacl-main.68 | OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering | https://aclanthology.org/2022.naacl-main.68/ | [
"Zhengbao Jiang",
"Yi Mao",
"Pengcheng He",
"Graham Neubig",
"Weizhu Chen"
] | The information in tables can be an important complement to text, making table-based question answering (QA) systems of great value. The intrinsic complexity of handling tables often adds an extra burden to both model design and data annotation. In this paper, we aim to develop a simple table-based QA model with minima... | 2022.naacl-main.68 | 10.18653/v1/2022.naacl-main.68 | null | 2207.03637 | title_snapshot |
2022.naacl-main.69 | Provably Confidential Language Modelling | https://aclanthology.org/2022.naacl-main.69/ | [
"Xuandong Zhao",
"Lei Li",
"Yu-Xiang Wang"
] | Large language models are shown to memorize privacy information such as social security numbers in training data. Given the sheer scale of the training corpus, it is challenging to screen and filter these privacy data, either manually or automatically. In this paper, we propose Confidentially Redacted Training (CRT), a... | 2022.naacl-main.69 | 10.18653/v1/2022.naacl-main.69 | null | 2205.01863 | title_snapshot |
2022.naacl-main.70 | KAT: A Knowledge Augmented Transformer for Vision-and-Language | https://aclanthology.org/2022.naacl-main.70/ | [
"Liangke Gui",
"Borui Wang",
"Qiuyuan Huang",
"Alexander Hauptmann",
"Yonatan Bisk",
"Jianfeng Gao"
] | The primary focus of recent work with large-scale transformers has been on optimizing the amount of information packed into the model’s parameters. In this work, we ask a complementary question: Can multimodal transformers leverage explicit knowledge in their reasoning? Existing, primarily unimodal, methods have explor... | 2022.naacl-main.70 | 10.18653/v1/2022.naacl-main.70 | null | 2112.08614 | title_snapshot |
2022.naacl-main.71 | When a sentence does not introduce a discourse entity, Transformer-based models still sometimes refer to it | https://aclanthology.org/2022.naacl-main.71/ | [
"Sebastian Schuster",
"Tal Linzen"
] | Understanding longer narratives or participating in conversations requires tracking of discourse entities that have been mentioned. Indefinite noun phrases (NPs), such as ‘a dog’, frequently introduce discourse entities but this behavior is modulated by sentential operators such as negation. For example, ‘a dog’ in ‘Ar... | 2022.naacl-main.71 | 10.18653/v1/2022.naacl-main.71 | null | 2205.03472 | title_snapshot |
2022.naacl-main.72 | On Curriculum Learning for Commonsense Reasoning | https://aclanthology.org/2022.naacl-main.72/ | [
"Adyasha Maharana",
"Mohit Bansal"
] | Commonsense reasoning tasks follow a standard paradigm of finetuning pretrained language models on the target task data, where samples are introduced to the model in a random order during training. However, recent research suggests that data order can have a significant impact on the performance of finetuned models for... | 2022.naacl-main.72 | 10.18653/v1/2022.naacl-main.72 | null | null | null |
2022.naacl-main.73 | DocTime: A Document-level Temporal Dependency Graph Parser | https://aclanthology.org/2022.naacl-main.73/ | [
"Puneet Mathur",
"Vlad Morariu",
"Verena Kaynig-Fittkau",
"Jiuxiang Gu",
"Franck Dernoncourt",
"Quan Tran",
"Ani Nenkova",
"Dinesh Manocha",
"Rajiv Jain"
] | We introduce DocTime - a novel temporal dependency graph (TDG) parser that takes as input a text document and produces a temporal dependency graph. It outperforms previous BERT-based solutions by a relative 4-8% on three datasets from modeling the problem as a graph network with path-prediction loss to incorporate long... | 2022.naacl-main.73 | 10.18653/v1/2022.naacl-main.73 | null | null | null |
2022.naacl-main.74 | FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization | https://aclanthology.org/2022.naacl-main.74/ | [
"David Wan",
"Mohit Bansal"
] | We present FactPEGASUS, an abstractive summarization model that addresses the problem of factuality during pre-training and fine-tuning: (1) We augment the sentence selection strategy of PEGASUS’s (Zhang et al., 2019) pre-training objective to create pseudo-summaries that are both important and factual; (2) We introduc... | 2022.naacl-main.74 | 10.18653/v1/2022.naacl-main.74 | null | 2205.07830 | title_snapshot |
2022.naacl-main.75 | ScAN: Suicide Attempt and Ideation Events Dataset | https://aclanthology.org/2022.naacl-main.75/ | [
"Bhanu Pratap Singh Rawat",
"Samuel Kovaly",
"Hong Yu",
"Wilfred Pigeon"
] | Suicide is an important public health concern and one of the leading causes of death worldwide. Suicidal behaviors, including suicide attempts (SA) and suicide ideations (SI), are leading risk factors for death by suicide. Information related to patients’ previous and current SA and SI are frequently documented in the ... | 2022.naacl-main.75 | 10.18653/v1/2022.naacl-main.75 | null | 2205.07872 | title_snapshot |
2022.naacl-main.76 | Socially Aware Bias Measurements for Hindi Language Representations | https://aclanthology.org/2022.naacl-main.76/ | [
"Vijit Malik",
"Sunipa Dev",
"Akihiro Nishi",
"Nanyun Peng",
"Kai-Wei Chang"
] | Language representations are an efficient tool used across NLP, but they are strife with encoded societal biases. These biases are studied extensively, but with a primary focus on English language representations and biases common in the context of Western society. In this work, we investigate the biases present in Hin... | 2022.naacl-main.76 | 10.18653/v1/2022.naacl-main.76 | null | 2110.07871 | title_snapshot |
2022.naacl-main.77 | AmbiPun: Generating Humorous Puns with Ambiguous Context | https://aclanthology.org/2022.naacl-main.77/ | [
"Anirudh Mittal",
"Yufei Tian",
"Nanyun Peng"
] | In this paper, we propose a simple yet effective way to generate pun sentences that does not require any training on existing puns. Our approach is inspired by humor theories that ambiguity comes from the context rather than the pun word itself. Given a pair of definitions of a pun word, our model first produces a list... | 2022.naacl-main.77 | 10.18653/v1/2022.naacl-main.77 | null | 2205.01825 | title_snapshot |
2022.naacl-main.78 | EmpHi: Generating Empathetic Responses with Human-like Intents | https://aclanthology.org/2022.naacl-main.78/ | [
"Mao Yan Chen",
"Siheng Li",
"Yujiu Yang"
] | In empathetic conversations, humans express their empathy to others with empathetic intents. However, most existing empathetic conversational methods suffer from a lack of empathetic intents, which leads to monotonous empathy. To address the bias of the empathetic intents distribution between empathetic dialogue models... | 2022.naacl-main.78 | 10.18653/v1/2022.naacl-main.78 | null | 2204.12191 | title_snapshot |
2022.naacl-main.79 | Yes, No or IDK: The Challenge of Unanswerable Yes/No Questions | https://aclanthology.org/2022.naacl-main.79/ | [
"Elior Sulem",
"Jamaal Hay",
"Dan Roth"
] | The Yes/No QA task (Clark et al., 2019) consists of “Yes” or “No” questions about a given context. However, in realistic scenarios, the information provided in the context is not always sufficient in order to answer the question. For example, given the context “She married a lawyer from New-York.”, we don’t know whethe... | 2022.naacl-main.79 | 10.18653/v1/2022.naacl-main.79 | null | null | null |
2022.naacl-main.80 | Inducing and Using Alignments for Transition-based AMR Parsing | https://aclanthology.org/2022.naacl-main.80/ | [
"Andrew Drozdov",
"Jiawei Zhou",
"Radu Florian",
"Andrew McCallum",
"Tahira Naseem",
"Yoon Kim",
"Ramón Astudillo"
] | Transition-based parsers for Abstract Meaning Representation (AMR) rely on node-to-word alignments. These alignments are learned separately from parser training and require a complex pipeline of rule-based components, pre-processing, and post-processing to satisfy domain-specific constraints. Parsers also train on a po... | 2022.naacl-main.80 | 10.18653/v1/2022.naacl-main.80 | null | 2205.01464 | title_snapshot |
2022.naacl-main.81 | Masked Part-Of-Speech Model: Does Modeling Long Context Help Unsupervised POS-tagging? | https://aclanthology.org/2022.naacl-main.81/ | [
"Xiang Zhou",
"Shiyue Zhang",
"Mohit Bansal"
] | Previous Part-Of-Speech (POS) induction models usually assume certain independence assumptions (e.g., Markov, unidirectional, local dependency) that do not hold in real languages. For example, the subject-verb agreement can be both long-term and bidirectional. To facilitate flexible dependency modeling, we propose a Ma... | 2022.naacl-main.81 | 10.18653/v1/2022.naacl-main.81 | null | 2206.14969 | title_snapshot |
2022.naacl-main.82 | DREAM: Improving Situational QA by First Elaborating the Situation | https://aclanthology.org/2022.naacl-main.82/ | [
"Yuling Gu",
"Bhavana Dalvi",
"Peter Clark"
] | When people answer questions about a specific situation, e.g., “I cheated on my mid-term exam last week. Was that wrong?”, cognitive science suggests that they form a mental picture of that situation before answering. While we do not know how language models (LMs) answer such questions, we conjecture that they may answ... | 2022.naacl-main.82 | 10.18653/v1/2022.naacl-main.82 | null | 2112.08656 | title_snapshot |
2022.naacl-main.83 | CoSe-Co: Text Conditioned Generative CommonSense Contextualizer | https://aclanthology.org/2022.naacl-main.83/ | [
"Rachit Bansal",
"Milan Aggarwal",
"Sumit Bhatia",
"Jivat Kaur",
"Balaji Krishnamurthy"
] | Pre-trained Language Models (PTLMs) have been shown to perform well on natural language tasks. Many prior works have leveraged structured commonsense present in the form of entities linked through labeled relations in Knowledge Graphs (KGs) to assist PTLMs. Retrieval approaches use KG as a separate static module which ... | 2022.naacl-main.83 | 10.18653/v1/2022.naacl-main.83 | null | 2206.05706 | title_snapshot |
2022.naacl-main.84 | Probing via Prompting | https://aclanthology.org/2022.naacl-main.84/ | [
"Jiaoda Li",
"Ryan Cotterell",
"Mrinmaya Sachan"
] | Probing is a popular approach to understand what linguistic information is contained in the representations of pre-trained language models. However, the mechanism of selecting the probe model has recently been subject to intense debate, as it is not clear if the probes are merely extracting information or modelling the... | 2022.naacl-main.84 | 10.18653/v1/2022.naacl-main.84 | null | 2207.01736 | title_snapshot |
2022.naacl-main.85 | Database Search Results Disambiguation for Task-Oriented Dialog Systems | https://aclanthology.org/2022.naacl-main.85/ | [
"Kun Qian",
"Satwik Kottur",
"Ahmad Beirami",
"Shahin Shayandeh",
"Paul Crook",
"Alborz Geramifard",
"Zhou Yu",
"Chinnadhurai Sankar"
] | As task-oriented dialog systems are becoming increasingly popular in our lives, more realistic tasks have been proposed and explored. However, new practical challenges arise. For instance, current dialog systems cannot effectively handle multiplesearch results when querying a database, due to the lack of such scenarios... | 2022.naacl-main.85 | 10.18653/v1/2022.naacl-main.85 | null | 2112.08351 | title_snapshot |
2022.naacl-main.86 | Unsupervised Slot Schema Induction for Task-oriented Dialog | https://aclanthology.org/2022.naacl-main.86/ | [
"Dian Yu",
"Mingqiu Wang",
"Yuan Cao",
"Izhak Shafran",
"Laurent Shafey",
"Hagen Soltau"
] | Carefully-designed schemas describing how to collect and annotate dialog corpora are a prerequisite towards building task-oriented dialog systems. In practical applications, manually designing schemas can be error-prone, laborious, iterative, and slow, especially when the schema is complicated. To alleviate this expens... | 2022.naacl-main.86 | 10.18653/v1/2022.naacl-main.86 | null | 2205.04515 | title_snapshot |
2022.naacl-main.87 | Towards a Progression-Aware Autonomous Dialogue Agent | https://aclanthology.org/2022.naacl-main.87/ | [
"Abraham Sanders",
"Tomek Strzalkowski",
"Mei Si",
"Albert Chang",
"Deepanshu Dey",
"Jonas Braasch",
"Dakuo Wang"
] | Recent advances in large-scale language modeling and generation have enabled the creation of dialogue agents that exhibit human-like responses in a wide range of conversational scenarios spanning a diverse set of tasks, from general chit-chat to focused goal-oriented discourse. While these agents excel at generating hi... | 2022.naacl-main.87 | 10.18653/v1/2022.naacl-main.87 | null | 2205.03692 | title_snapshot |
2022.naacl-main.88 | Cross-Domain Detection of GPT-2-Generated Technical Text | https://aclanthology.org/2022.naacl-main.88/ | [
"Juan Diego Rodriguez",
"Todd Hay",
"David Gros",
"Zain Shamsi",
"Ravi Srinivasan"
] | Machine-generated text presents a potential threat not only to the public sphere, but also to the scientific enterprise, whereby genuine research is undermined by convincing, synthetic text. In this paper we examine the problem of detecting GPT-2-generated technical research text. We first consider the realistic scenar... | 2022.naacl-main.88 | 10.18653/v1/2022.naacl-main.88 | null | null | null |
2022.naacl-main.89 | DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions | https://aclanthology.org/2022.naacl-main.89/ | [
"Neha Nayak Kennard",
"Tim O’Gorman",
"Rajarshi Das",
"Akshay Sharma",
"Chhandak Bagchi",
"Matthew Clinton",
"Pranay Kumar Yelugam",
"Hamed Zamani",
"Andrew McCallum"
] | At the foundation of scientific evaluation is the labor-intensive process of peer review. This critical task requires participants to consume vast amounts of highly technical text. Prior work has annotated different aspects of review argumentation, but discourse relations between reviews and rebuttals have yet to be ex... | 2022.naacl-main.89 | 10.18653/v1/2022.naacl-main.89 | null | 2110.08520 | title_snapshot |
2022.naacl-main.90 | MultiSpanQA: A Dataset for Multi-Span Question Answering | https://aclanthology.org/2022.naacl-main.90/ | [
"Haonan Li",
"Martin Tomko",
"Maria Vasardani",
"Timothy Baldwin"
] | Most existing reading comprehension datasets focus on single-span answers, which can be extracted as a single contiguous span from a given text passage. Multi-span questions, i.e., questions whose answer is a series of multiple discontiguous spans in the text, are common real life but are less studied. In this paper, w... | 2022.naacl-main.90 | 10.18653/v1/2022.naacl-main.90 | null | null | null |
2022.naacl-main.91 | Context-Aware Abbreviation Expansion Using Large Language Models | https://aclanthology.org/2022.naacl-main.91/ | [
"Shanqing Cai",
"Subhashini Venugopalan",
"Katrin Tomanek",
"Ajit Narayanan",
"Meredith Morris",
"Michael Brenner"
] | Motivated by the need for accelerating text entry in augmentative and alternative communication (AAC) for people with severe motor impairments, we propose a paradigm in which phrases are abbreviated aggressively as primarily word-initial letters. Our approach is to expand the abbreviations into full-phrase options by l... | 2022.naacl-main.91 | 10.18653/v1/2022.naacl-main.91 | null | 2205.03767 | title_snapshot |
2022.naacl-main.92 | Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Models | https://aclanthology.org/2022.naacl-main.92/ | [
"Yang Trista Cao",
"Anna Sotnikova",
"Hal Daumé III",
"Rachel Rudinger",
"Linda Zou"
] | NLP models trained on text have been shown to reproduce human stereotypes, which can magnify harms to marginalized groups when systems are deployed at scale. We adapt the Agency-Belief-Communion (ABC) stereotype model of Koch et al. (2016) from social psychology as a framework for the systematic study and discovery of ... | 2022.naacl-main.92 | 10.18653/v1/2022.naacl-main.92 | null | 2206.11684 | title_snapshot |
2022.naacl-main.93 | Sort by Structure: Language Model Ranking as Dependency Probing | https://aclanthology.org/2022.naacl-main.93/ | [
"Max Müller-Eberstein",
"Rob van der Goot",
"Barbara Plank"
] | Making an informed choice of pre-trained language model (LM) is critical for performance, yet environmentally costly, and as such widely underexplored. The field of Computer Vision has begun to tackle encoder ranking, with promising forays into Natural Language Processing, however they lack coverage of linguistic tasks... | 2022.naacl-main.93 | 10.18653/v1/2022.naacl-main.93 | null | 2206.04935 | title_snapshot |
2022.naacl-main.94 | Quantifying Synthesis and Fusion and their Impact on Machine Translation | https://aclanthology.org/2022.naacl-main.94/ | [
"Arturo Oncevay",
"Duygu Ataman",
"Niels Van Berkel",
"Barry Haddow",
"Alexandra Birch",
"Johannes Bjerva"
] | Theoretical work in morphological typology offers the possibility of measuring morphological diversity on a continuous scale. However, literature in Natural Language Processing (NLP) typically labels a whole language with a strict type of morphology, e.g. fusional or agglutinative. In this work, we propose to reduce th... | 2022.naacl-main.94 | 10.18653/v1/2022.naacl-main.94 | null | 2205.03369 | title_snapshot |
2022.naacl-main.95 | Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation | https://aclanthology.org/2022.naacl-main.95/ | [
"Deeksha Varshney",
"Akshara Prabhakar",
"Asif Ekbal"
] | Grounding dialogue on external knowledge and interpreting linguistic patterns in dialogue history context, such as ellipsis, anaphora, and co-reference is critical for dialogue comprehension and generation. In this paper, we present a novel open-domain dialogue generation model which effectively utilizes the large-scal... | 2022.naacl-main.95 | 10.18653/v1/2022.naacl-main.95 | null | 2205.13928 | title_snapshot |
2022.naacl-main.96 | Efficient Hierarchical Domain Adaptation for Pretrained Language Models | https://aclanthology.org/2022.naacl-main.96/ | [
"Alexandra Chronopoulou",
"Matthew Peters",
"Jesse Dodge"
] | The remarkable success of large language models has been driven by dense models trained on massive unlabeled, unstructured corpora. These corpora typically contain text from diverse, heterogeneous sources, but information about the source of the text is rarely used during training. Transferring their knowledge to a tar... | 2022.naacl-main.96 | 10.18653/v1/2022.naacl-main.96 | null | 2112.08786 | title_snapshot |
2022.naacl-main.97 | Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-Based Hate | https://aclanthology.org/2022.naacl-main.97/ | [
"Hannah Kirk",
"Bertie Vidgen",
"Paul Rottger",
"Tristan Thrush",
"Scott A. Hale"
] | Detecting online hate is a complex task, and low-performing models have harmful consequences when used for sensitive applications such as content moderation. Emoji-based hate is an emerging challenge for automated detection. We present HatemojiCheck, a test suite of 3,930 short-form statements that allows us to evaluat... | 2022.naacl-main.97 | 10.18653/v1/2022.naacl-main.97 | null | 2108.05921 | title_snapshot |
2022.naacl-main.98 | On the Economics of Multilingual Few-shot Learning: Modeling the Cost-Performance Trade-offs of Machine Translated and Manual Data | https://aclanthology.org/2022.naacl-main.98/ | [
"Kabir Ahuja",
"Monojit Choudhury",
"Sandipan Dandapat"
] | Borrowing ideas from Production functions in micro-economics, in this paper we introduce a framework to systematically evaluate the performance and cost trade-offs between machine-translated and manually-created labelled data for task-specific fine-tuning of massively multilingual language models. We illustrate the eff... | 2022.naacl-main.98 | 10.18653/v1/2022.naacl-main.98 | null | 2205.06350 | title_snapshot |
2022.naacl-main.99 | Learning to Selectively Learn for Weakly Supervised Paraphrase Generation with Model-based Reinforcement Learning | https://aclanthology.org/2022.naacl-main.99/ | [
"Haiyan Yin",
"Dingcheng Li",
"Ping Li"
] | Paraphrase generation is an important language generation task attempting to interpret user intents and systematically generate new phrases of identical meanings to the given ones. However, the effectiveness of paraphrase generation is constrained by the access to the golden labeled data pairs where both the amount and... | 2022.naacl-main.99 | 10.18653/v1/2022.naacl-main.99 | null | null | null |
2022.naacl-main.100 | Quality-Aware Decoding for Neural Machine Translation | https://aclanthology.org/2022.naacl-main.100/ | [
"Patrick Fernandes",
"António Farinhas",
"Ricardo Rei",
"José G. C. de Souza",
"Perez Ogayo",
"Graham Neubig",
"Andre Martins"
] | Despite the progress in machine translation quality estimation and evaluation in the last years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers around finding the most probable translation according to the model (MAP decoding), approximated with beam search. In this paper, we bring... | 2022.naacl-main.100 | 10.18653/v1/2022.naacl-main.100 | null | 2205.00978 | title_snapshot |
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