paper_id stringlengths 15 17 | title stringlengths 26 142 | paper_url stringlengths 41 43 | authors listlengths 1 23 | abstract large_stringlengths 289 1.7k | anthology_id stringlengths 15 17 | doi stringlengths 27 29 | award stringclasses 5
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2022.acl-long.1 | AdapLeR: Speeding up Inference by Adaptive Length Reduction | https://aclanthology.org/2022.acl-long.1/ | [
"Ali Modarressi",
"Hosein Mohebbi",
"Mohammad Taher Pilehvar"
] | Pre-trained language models have shown stellar performance in various downstream tasks. But, this usually comes at the cost of high latency and computation, hindering their usage in resource-limited settings. In this work, we propose a novel approach for reducing the computational cost of BERT with minimal loss in down... | 2022.acl-long.1 | 10.18653/v1/2022.acl-long.1 | null | 2203.08991 | title_snapshot |
2022.acl-long.2 | Quantified Reproducibility Assessment of NLP Results | https://aclanthology.org/2022.acl-long.2/ | [
"Anya Belz",
"Maja Popovic",
"Simon Mille"
] | This paper describes and tests a method for carrying out quantified reproducibility assessment (QRA) that is based on concepts and definitions from metrology. QRA produces a single score estimating the degree of reproducibility of a given system and evaluation measure, on the basis of the scores from, and differences b... | 2022.acl-long.2 | 10.18653/v1/2022.acl-long.2 | null | 2204.05961 | title_snapshot |
2022.acl-long.3 | Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddings | https://aclanthology.org/2022.acl-long.3/ | [
"Sangwon Yu",
"Jongyoon Song",
"Heeseung Kim",
"Seongmin Lee",
"Woo-Jong Ryu",
"Sungroh Yoon"
] | Recent studies have determined that the learned token embeddings of large-scale neural language models are degenerated to be anisotropic with a narrow-cone shape. This phenomenon, called the representation degeneration problem, facilitates an increase in the overall similarity between token embeddings that negatively a... | 2022.acl-long.3 | 10.18653/v1/2022.acl-long.3 | null | 2109.03127 | title_snapshot |
2022.acl-long.4 | AlephBERT: Language Model Pre-training and Evaluation from Sub-Word to Sentence Level | https://aclanthology.org/2022.acl-long.4/ | [
"Amit Seker",
"Elron Bandel",
"Dan Bareket",
"Idan Brusilovsky",
"Refael Greenfeld",
"Reut Tsarfaty"
] | Large Pre-trained Language Models (PLMs) have become ubiquitous in the development of language understanding technology and lie at the heart of many artificial intelligence advances. While advances reported for English using PLMs are unprecedented, reported advances using PLMs for Hebrew are few and far between. The pr... | 2022.acl-long.4 | 10.18653/v1/2022.acl-long.4 | null | null | null |
2022.acl-long.5 | Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning | https://aclanthology.org/2022.acl-long.5/ | [
"Moxin Li",
"Fuli Feng",
"Hanwang Zhang",
"Xiangnan He",
"Fengbin Zhu",
"Tat-Seng Chua"
] | Neural discrete reasoning (NDR) has shown remarkable progress in combining deep models with discrete reasoning. However, we find that existing NDR solution suffers from large performance drop on hypothetical questions, e.g. “what the annualized rate of return would be if the revenue in 2020 was doubled”. The key to hyp... | 2022.acl-long.5 | 10.18653/v1/2022.acl-long.5 | null | null | null |
2022.acl-long.6 | Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification | https://aclanthology.org/2022.acl-long.6/ | [
"George-Eduard Zaharia",
"Răzvan-Alexandru Smădu",
"Dumitru Cercel",
"Mihai Dascalu"
] | Complex word identification (CWI) is a cornerstone process towards proper text simplification. CWI is highly dependent on context, whereas its difficulty is augmented by the scarcity of available datasets which vary greatly in terms of domains and languages. As such, it becomes increasingly more difficult to develop a ... | 2022.acl-long.6 | 10.18653/v1/2022.acl-long.6 | null | 2205.07283 | title_snapshot |
2022.acl-long.7 | JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection | https://aclanthology.org/2022.acl-long.7/ | [
"Bin Liang",
"Qinglin Zhu",
"Xiang Li",
"Min Yang",
"Lin Gui",
"Yulan He",
"Ruifeng Xu"
] | Zero-shot stance detection (ZSSD) aims to detect the stance for an unseen target during the inference stage. In this paper, we propose a joint contrastive learning (JointCL) framework, which consists of stance contrastive learning and target-aware prototypical graph contrastive learning. Specifically, a stance contrast... | 2022.acl-long.7 | 10.18653/v1/2022.acl-long.7 | null | null | null |
2022.acl-long.8 | [CASPI] Causal-aware Safe Policy Improvement for Task-oriented Dialogue | https://aclanthology.org/2022.acl-long.8/ | [
"Govardana Sachithanandam Ramachandran",
"Kazuma Hashimoto",
"Caiming Xiong"
] | The recent success of reinforcement learning (RL) in solving complex tasks is often attributed to its capacity to explore and exploit an environment. Sample efficiency is usually not an issue for tasks with cheap simulators to sample data online. On the other hand, Task-oriented Dialogues (ToD) are usually learnt from ... | 2022.acl-long.8 | 10.18653/v1/2022.acl-long.8 | null | 2103.06370 | title_judge |
2022.acl-long.9 | UniTranSeR: A Unified Transformer Semantic Representation Framework for Multimodal Task-Oriented Dialog System | https://aclanthology.org/2022.acl-long.9/ | [
"Zhiyuan Ma",
"Jianjun Li",
"Guohui Li",
"Yongjing Cheng"
] | As a more natural and intelligent interaction manner, multimodal task-oriented dialog system recently has received great attention and many remarkable progresses have been achieved. Nevertheless, almost all existing studies follow the pipeline to first learn intra-modal features separately and then conduct simple featu... | 2022.acl-long.9 | 10.18653/v1/2022.acl-long.9 | null | null | null |
2022.acl-long.10 | Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking | https://aclanthology.org/2022.acl-long.10/ | [
"Yue Feng",
"Aldo Lipani",
"Fanghua Ye",
"Qiang Zhang",
"Emine Yilmaz"
] | Dialogue State Tracking (DST) aims to keep track of users’ intentions during the course of a conversation. In DST, modelling the relations among domains and slots is still an under-studied problem. Existing approaches that have considered such relations generally fall short in: (1) fusing prior slot-domain membership r... | 2022.acl-long.10 | 10.18653/v1/2022.acl-long.10 | null | 2204.06677 | title_snapshot |
2022.acl-long.11 | Attention Temperature Matters in Abstractive Summarization Distillation | https://aclanthology.org/2022.acl-long.11/ | [
"Shengqiang Zhang",
"Xingxing Zhang",
"Hangbo Bao",
"Furu Wei"
] | Recent progress of abstractive text summarization largely relies on large pre-trained sequence-to-sequence Transformer models, which are computationally expensive. This paper aims to distill these large models into smaller ones for faster inference and with minimal performance loss. Pseudo-labeling based methods are po... | 2022.acl-long.11 | 10.18653/v1/2022.acl-long.11 | null | 2106.03441 | title_snapshot |
2022.acl-long.12 | Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation | https://aclanthology.org/2022.acl-long.12/ | [
"Guanhua Chen",
"Shuming Ma",
"Yun Chen",
"Dongdong Zhang",
"Jia Pan",
"Wenping Wang",
"Furu Wei"
] | This paper demonstrates that multilingual pretraining and multilingual fine-tuning are both critical for facilitating cross-lingual transfer in zero-shot translation, where the neural machine translation (NMT) model is tested on source languages unseen during supervised training. Following this idea, we present SixT+, ... | 2022.acl-long.12 | 10.18653/v1/2022.acl-long.12 | null | 2110.08547 | title_judge |
2022.acl-long.13 | TopWORDS-Seg: Simultaneous Text Segmentation and Word Discovery for Open-Domain Chinese Texts via Bayesian Inference | https://aclanthology.org/2022.acl-long.13/ | [
"Changzai Pan",
"Maosong Sun",
"Ke Deng"
] | Processing open-domain Chinese texts has been a critical bottleneck in computational linguistics for decades, partially because text segmentation and word discovery often entangle with each other in this challenging scenario. No existing methods yet can achieve effective text segmentation and word discovery simultaneou... | 2022.acl-long.13 | 10.18653/v1/2022.acl-long.13 | null | null | null |
2022.acl-long.14 | An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition | https://aclanthology.org/2022.acl-long.14/ | [
"Zhuoran Li",
"Chunming Hu",
"Xiaohui Guo",
"Junfan Chen",
"Wenyi Qin",
"Richong Zhang"
] | Cross-lingual named entity recognition task is one of the critical problems for evaluating the potential transfer learning techniques on low resource languages. Knowledge distillation using pre-trained multilingual language models between source and target languages have shown their superiority in transfer. However, ex... | 2022.acl-long.14 | 10.18653/v1/2022.acl-long.14 | null | null | null |
2022.acl-long.15 | Discriminative Marginalized Probabilistic Neural Method for Multi-Document Summarization of Medical Literature | https://aclanthology.org/2022.acl-long.15/ | [
"Gianluca Moro",
"Luca Ragazzi",
"Lorenzo Valgimigli",
"Davide Freddi"
] | Although current state-of-the-art Transformer-based solutions succeeded in a wide range for single-document NLP tasks, they still struggle to address multi-input tasks such as multi-document summarization. Many solutions truncate the inputs, thus ignoring potential summary-relevant contents, which is unacceptable in th... | 2022.acl-long.15 | 10.18653/v1/2022.acl-long.15 | null | null | null |
2022.acl-long.16 | Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm | https://aclanthology.org/2022.acl-long.16/ | [
"Shaoyi Huang",
"Dongkuan Xu",
"Ian Yen",
"Yijue Wang",
"Sung-En Chang",
"Bingbing Li",
"Shiyang Chen",
"Mimi Xie",
"Sanguthevar Rajasekaran",
"Hang Liu",
"Caiwen Ding"
] | Conventional wisdom in pruning Transformer-based language models is that pruning reduces the model expressiveness and thus is more likely to underfit rather than overfit. However, under the trending pretrain-and-finetune paradigm, we postulate a counter-traditional hypothesis, that is: pruning increases the risk of ove... | 2022.acl-long.16 | 10.18653/v1/2022.acl-long.16 | null | 2110.08190 | title_snapshot |
2022.acl-long.17 | CipherDAug: Ciphertext based Data Augmentation for Neural Machine Translation | https://aclanthology.org/2022.acl-long.17/ | [
"Nishant Kambhatla",
"Logan Born",
"Anoop Sarkar"
] | We propose a novel data-augmentation technique for neural machine translation based on ROT-k ciphertexts. ROT-k is a simple letter substitution cipher that replaces a letter in the plaintext with the kth letter after it in the alphabet. We first generate multiple ROT-k ciphertexts using different values of k for the pl... | 2022.acl-long.17 | 10.18653/v1/2022.acl-long.17 | null | 2204.00665 | title_snapshot |
2022.acl-long.18 | Overlap-based Vocabulary Generation Improves Cross-lingual Transfer Among Related Languages | https://aclanthology.org/2022.acl-long.18/ | [
"Vaidehi Patil",
"Partha Talukdar",
"Sunita Sarawagi"
] | Pre-trained multilingual language models such as mBERT and XLM-R have demonstrated great potential for zero-shot cross-lingual transfer to low web-resource languages (LRL). However, due to limited model capacity, the large difference in the sizes of available monolingual corpora between high web-resource languages (HRL... | 2022.acl-long.18 | 10.18653/v1/2022.acl-long.18 | null | 2203.01976 | title_snapshot |
2022.acl-long.19 | Long-range Sequence Modeling with Predictable Sparse Attention | https://aclanthology.org/2022.acl-long.19/ | [
"Yimeng Zhuang",
"Jing Zhang",
"Mei Tu"
] | Self-attention mechanism has been shown to be an effective approach for capturing global context dependencies in sequence modeling, but it suffers from quadratic complexity in time and memory usage. Due to the sparsity of the attention matrix, much computation is redundant. Therefore, in this paper, we design an effici... | 2022.acl-long.19 | 10.18653/v1/2022.acl-long.19 | null | null | null |
2022.acl-long.20 | Improving Personalized Explanation Generation through Visualization | https://aclanthology.org/2022.acl-long.20/ | [
"Shijie Geng",
"Zuohui Fu",
"Yingqiang Ge",
"Lei Li",
"Gerard de Melo",
"Yongfeng Zhang"
] | In modern recommender systems, there are usually comments or reviews from users that justify their ratings for different items. Trained on such textual corpus, explainable recommendation models learn to discover user interests and generate personalized explanations. Though able to provide plausible explanations, existi... | 2022.acl-long.20 | 10.18653/v1/2022.acl-long.20 | null | null | null |
2022.acl-long.21 | New Intent Discovery with Pre-training and Contrastive Learning | https://aclanthology.org/2022.acl-long.21/ | [
"Yuwei Zhang",
"Haode Zhang",
"Li-Ming Zhan",
"Xiao-Ming Wu",
"Albert Lam"
] | New intent discovery aims to uncover novel intent categories from user utterances to expand the set of supported intent classes. It is a critical task for the development and service expansion of a practical dialogue system. Despite its importance, this problem remains under-explored in the literature. Existing approac... | 2022.acl-long.21 | 10.18653/v1/2022.acl-long.21 | null | 2205.12914 | title_snapshot |
2022.acl-long.22 | Modeling U.S. State-Level Policies by Extracting Winners and Losers from Legislative Texts | https://aclanthology.org/2022.acl-long.22/ | [
"Maryam Davoodi",
"Eric Waltenburg",
"Dan Goldwasser"
] | Decisions on state-level policies have a deep effect on many aspects of our everyday life, such as health-care and education access. However, there is little understanding of how these policies and decisions are being formed in the legislative process. We take a data-driven approach by decoding the impact of legislatio... | 2022.acl-long.22 | 10.18653/v1/2022.acl-long.22 | null | null | null |
2022.acl-long.23 | Structural Characterization for Dialogue Disentanglement | https://aclanthology.org/2022.acl-long.23/ | [
"Xinbei Ma",
"Zhuosheng Zhang",
"Hai Zhao"
] | Tangled multi-party dialogue contexts lead to challenges for dialogue reading comprehension, where multiple dialogue threads flow simultaneously within a common dialogue record, increasing difficulties in understanding the dialogue history for both human and machine. Previous studies mainly focus on utterance encoding ... | 2022.acl-long.23 | 10.18653/v1/2022.acl-long.23 | null | 2110.08018 | title_snapshot |
2022.acl-long.24 | Multi-Party Empathetic Dialogue Generation: A New Task for Dialog Systems | https://aclanthology.org/2022.acl-long.24/ | [
"Ling.Yu Zhu",
"Zhengkun Zhang",
"Jun Wang",
"Hongbin Wang",
"Haiying Wu",
"Zhenglu Yang"
] | Empathetic dialogue assembles emotion understanding, feeling projection, and appropriate response generation. Existing work for empathetic dialogue generation concentrates on the two-party conversation scenario. Multi-party dialogues, however, are pervasive in reality. Furthermore, emotion and sensibility are typically... | 2022.acl-long.24 | 10.18653/v1/2022.acl-long.24 | null | null | null |
2022.acl-long.25 | MISC: A Mixed Strategy-Aware Model integrating COMET for Emotional Support Conversation | https://aclanthology.org/2022.acl-long.25/ | [
"Quan Tu",
"Yanran Li",
"Jianwei Cui",
"Bin Wang",
"Ji-Rong Wen",
"Rui Yan"
] | Applying existing methods to emotional support conversation—which provides valuable assistance to people who are in need—has two major limitations: (a) they generally employ a conversation-level emotion label, which is too coarse-grained to capture user’s instant mental state; (b) most of them focus on expressing empat... | 2022.acl-long.25 | 10.18653/v1/2022.acl-long.25 | null | 2203.13560 | title_snapshot |
2022.acl-long.26 | GLM: General Language Model Pretraining with Autoregressive Blank Infilling | https://aclanthology.org/2022.acl-long.26/ | [
"Zhengxiao Du",
"Yujie Qian",
"Xiao Liu",
"Ming Ding",
"Jiezhong Qiu",
"Zhilin Yang",
"Jie Tang"
] | There have been various types of pretraining architectures including autoencoding models (e.g., BERT), autoregressive models (e.g., GPT), and encoder-decoder models (e.g., T5). However, none of the pretraining frameworks performs the best for all tasks of three main categories including natural language understanding (... | 2022.acl-long.26 | 10.18653/v1/2022.acl-long.26 | null | 2103.10360 | title_snapshot |
2022.acl-long.27 | QuoteR: A Benchmark of Quote Recommendation for Writing | https://aclanthology.org/2022.acl-long.27/ | [
"Fanchao Qi",
"Yanhui Yang",
"Jing Yi",
"Zhili Cheng",
"Zhiyuan Liu",
"Maosong Sun"
] | It is very common to use quotations (quotes) to make our writings more elegant or convincing. To help people find appropriate quotes efficiently, the task of quote recommendation is presented, aiming to recommend quotes that fit the current context of writing. There have been various quote recommendation approaches, bu... | 2022.acl-long.27 | 10.18653/v1/2022.acl-long.27 | null | 2202.13145 | title_snapshot |
2022.acl-long.28 | Towards Comprehensive Patent Approval Predictions:Beyond Traditional Document Classification | https://aclanthology.org/2022.acl-long.28/ | [
"Xiaochen Gao",
"Zhaoyi Hou",
"Yifei Ning",
"Kewen Zhao",
"Beilei He",
"Jingbo Shang",
"Vish Krishnan"
] | Predicting the approval chance of a patent application is a challenging problem involving multiple facets. The most crucial facet is arguably the novelty — 35 U.S. Code § 102 rejects more recent applications that have very similar prior arts. Such novelty evaluations differ the patent approval prediction from conventio... | 2022.acl-long.28 | 10.18653/v1/2022.acl-long.28 | null | null | null |
2022.acl-long.29 | Hypergraph Transformer: Weakly-Supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering | https://aclanthology.org/2022.acl-long.29/ | [
"Yu-Jung Heo",
"Eun-Sol Kim",
"Woo Suk Choi",
"Byoung-Tak Zhang"
] | Knowledge-based visual question answering (QA) aims to answer a question which requires visually-grounded external knowledge beyond image content itself. Answering complex questions that require multi-hop reasoning under weak supervision is considered as a challenging problem since i) no supervision is given to the rea... | 2022.acl-long.29 | 10.18653/v1/2022.acl-long.29 | null | 2204.10448 | title_snapshot |
2022.acl-long.30 | Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech | https://aclanthology.org/2022.acl-long.30/ | [
"Yang Li",
"Cheng Yu",
"Guangzhi Sun",
"Hua Jiang",
"Fanglei Sun",
"Weiqin Zu",
"Ying Wen",
"Yang Yang",
"Jun Wang"
] | Modelling prosody variation is critical for synthesizing natural and expressive speech in end-to-end text-to-speech (TTS) systems. In this paper, a cross-utterance conditional VAE (CUC-VAE) is proposed to estimate a posterior probability distribution of the latent prosody features for each phoneme by conditioning on ac... | 2022.acl-long.30 | 10.18653/v1/2022.acl-long.30 | null | 2205.04120 | title_snapshot |
2022.acl-long.31 | Mix and Match: Learning-free Controllable Text Generationusing Energy Language Models | https://aclanthology.org/2022.acl-long.31/ | [
"Fatemehsadat Mireshghallah",
"Kartik Goyal",
"Taylor Berg-Kirkpatrick"
] | Recent work on controlled text generation has either required attribute-based fine-tuning of the base language model (LM), or has restricted the parameterization of the attribute discriminator to be compatible with the base autoregressive LM. In this work, we propose Mix and Match LM, a global score-based alternative f... | 2022.acl-long.31 | 10.18653/v1/2022.acl-long.31 | null | 2203.13299 | title_judge |
2022.acl-long.32 | So Different Yet So Alike! Constrained Unsupervised Text Style Transfer | https://aclanthology.org/2022.acl-long.32/ | [
"Abhinav Ramesh Kashyap",
"Devamanyu Hazarika",
"Min-Yen Kan",
"Roger Zimmermann",
"Soujanya Poria"
] | Automatic transfer of text between domains has become popular in recent times. One of its aims is to preserve the semantic content while adapting to the target domain. However, it does not explicitly maintain other attributes between the source and translated text: e.g., text length and descriptiveness. Maintaining con... | 2022.acl-long.32 | 10.18653/v1/2022.acl-long.32 | null | 2205.04093 | title_snapshot |
2022.acl-long.33 | e-CARE: a New Dataset for Exploring Explainable Causal Reasoning | https://aclanthology.org/2022.acl-long.33/ | [
"Li Du",
"Xiao Ding",
"Kai Xiong",
"Ting Liu",
"Bing Qin"
] | Understanding causality has vital importance for various Natural Language Processing (NLP) applications. Beyond the labeled instances, conceptual explanations of the causality can provide deep understanding of the causal fact to facilitate the causal reasoning process. However, such explanation information still remain... | 2022.acl-long.33 | 10.18653/v1/2022.acl-long.33 | null | 2205.05849 | title_snapshot |
2022.acl-long.34 | Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension | https://aclanthology.org/2022.acl-long.34/ | [
"Ying Xu",
"Dakuo Wang",
"Mo Yu",
"Daniel Ritchie",
"Bingsheng Yao",
"Tongshuang Wu",
"Zheng Zhang",
"Toby Jia-Jun Li",
"Nora Bradford",
"Branda Sun",
"Tran Bao Hoang",
"Yisi Sang",
"Yufang Hou",
"Xiaojuan Ma",
"Diyi Yang",
"Nanyun Peng",
"Zhou Yu",
"Mark Warschauer"
] | Question answering (QA) is a fundamental means to facilitate assessment and training of narrative comprehension skills for both machines and young children, yet there is scarcity of high-quality QA datasets carefully designed to serve this purpose. In particular, existing datasets rarely distinguish fine-grained readin... | 2022.acl-long.34 | 10.18653/v1/2022.acl-long.34 | null | 2203.13947 | title_snapshot |
2022.acl-long.35 | KaFSP: Knowledge-Aware Fuzzy Semantic Parsing for Conversational Question Answering over a Large-Scale Knowledge Base | https://aclanthology.org/2022.acl-long.35/ | [
"Junzhuo Li",
"Deyi Xiong"
] | In this paper, we study two issues of semantic parsing approaches to conversational question answering over a large-scale knowledge base: (1) The actions defined in grammar are not sufficient to handle uncertain reasoning common in real-world scenarios. (2) Knowledge base information is not well exploited and incorpora... | 2022.acl-long.35 | 10.18653/v1/2022.acl-long.35 | null | null | null |
2022.acl-long.36 | Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment | https://aclanthology.org/2022.acl-long.36/ | [
"Zijie Huang",
"Zheng Li",
"Haoming Jiang",
"Tianyu Cao",
"Hanqing Lu",
"Bing Yin",
"Karthik Subbian",
"Yizhou Sun",
"Wei Wang"
] | Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from complete. Due to labor-intensive human labeling, this phenomenon deteriorates when handling knowledge represented in various languages. In this paper, we explore multilingual KG completion, which leverages limited seed alignment as... | 2022.acl-long.36 | 10.18653/v1/2022.acl-long.36 | null | 2203.14987 | title_snapshot |
2022.acl-long.37 | Modeling Hierarchical Syntax Structure with Triplet Position for Source Code Summarization | https://aclanthology.org/2022.acl-long.37/ | [
"Juncai Guo",
"Jin Liu",
"Yao Wan",
"Li Li",
"Pingyi Zhou"
] | Automatic code summarization, which aims to describe the source code in natural language, has become an essential task in software maintenance. Our fellow researchers have attempted to achieve such a purpose through various machine learning-based approaches. One key challenge keeping these approaches from being practic... | 2022.acl-long.37 | 10.18653/v1/2022.acl-long.37 | null | null | null |
2022.acl-long.38 | FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding | https://aclanthology.org/2022.acl-long.38/ | [
"Yanan Zheng",
"Jing Zhou",
"Yujie Qian",
"Ming Ding",
"Chonghua Liao",
"Li Jian",
"Ruslan Salakhutdinov",
"Jie Tang",
"Sebastian Ruder",
"Zhilin Yang"
] | The few-shot natural language understanding (NLU) task has attracted much recent attention. However, prior methods have been evaluated under a disparate set of protocols, which hinders fair comparison and measuring the progress of the field. To address this issue, we introduce an evaluation framework that improves prev... | 2022.acl-long.38 | 10.18653/v1/2022.acl-long.38 | null | 2109.12742 | title_snapshot |
2022.acl-long.39 | Learn to Adapt for Generalized Zero-Shot Text Classification | https://aclanthology.org/2022.acl-long.39/ | [
"Yiwen Zhang",
"Caixia Yuan",
"Xiaojie Wang",
"Ziwei Bai",
"Yongbin Liu"
] | Generalized zero-shot text classification aims to classify textual instances from both previously seen classes and incrementally emerging unseen classes. Most existing methods generalize poorly since the learned parameters are only optimal for seen classes rather than for both classes, and the parameters keep stationar... | 2022.acl-long.39 | 10.18653/v1/2022.acl-long.39 | null | null | null |
2022.acl-long.40 | TableFormer: Robust Transformer Modeling for Table-Text Encoding | https://aclanthology.org/2022.acl-long.40/ | [
"Jingfeng Yang",
"Aditya Gupta",
"Shyam Upadhyay",
"Luheng He",
"Rahul Goel",
"Shachi Paul"
] | Understanding tables is an important aspect of natural language understanding. Existing models for table understanding require linearization of the table structure, where row or column order is encoded as an unwanted bias. Such spurious biases make the model vulnerable to row and column order perturbations. Additionall... | 2022.acl-long.40 | 10.18653/v1/2022.acl-long.40 | null | 2203.00274 | title_snapshot |
2022.acl-long.41 | Perceiving the World: Question-guided Reinforcement Learning for Text-based Games | https://aclanthology.org/2022.acl-long.41/ | [
"Yunqiu Xu",
"Meng Fang",
"Ling Chen",
"Yali Du",
"Joey Zhou",
"Chengqi Zhang"
] | Text-based games provide an interactive way to study natural language processing. While deep reinforcement learning has shown effectiveness in developing the game playing agent, the low sample efficiency and the large action space remain to be the two major challenges that hinder the DRL from being applied in the real ... | 2022.acl-long.41 | 10.18653/v1/2022.acl-long.41 | null | 2204.09597 | title_snapshot |
2022.acl-long.42 | Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization | https://aclanthology.org/2022.acl-long.42/ | [
"Ruipeng Jia",
"Xingxing Zhang",
"Yanan Cao",
"Zheng Lin",
"Shi Wang",
"Furu Wei"
] | In zero-shot multilingual extractive text summarization, a model is typically trained on English summarization dataset and then applied on summarization datasets of other languages. Given English gold summaries and documents, sentence-level labels for extractive summarization are usually generated using heuristics. How... | 2022.acl-long.42 | 10.18653/v1/2022.acl-long.42 | null | 2204.13512 | title_snapshot |
2022.acl-long.43 | Few-Shot Class-Incremental Learning for Named Entity Recognition | https://aclanthology.org/2022.acl-long.43/ | [
"Rui Wang",
"Tong Yu",
"Handong Zhao",
"Sungchul Kim",
"Subrata Mitra",
"Ruiyi Zhang",
"Ricardo Henao"
] | Previous work of class-incremental learning for Named Entity Recognition (NER) relies on the assumption that there exists abundance of labeled data for the training of new classes. In this work, we study a more challenging but practical problem, i.e., few-shot class-incremental learning for NER, where an NER model is t... | 2022.acl-long.43 | 10.18653/v1/2022.acl-long.43 | null | null | null |
2022.acl-long.44 | Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation | https://aclanthology.org/2022.acl-long.44/ | [
"Yingxiu Zhao",
"Zhiliang Tian",
"Huaxiu Yao",
"Yinhe Zheng",
"Dongkyu Lee",
"Yiping Song",
"Jian Sun",
"Nevin Zhang"
] | Building models of natural language processing (NLP) is challenging in low-resource scenarios where limited data are available. Optimization-based meta-learning algorithms achieve promising results in low-resource scenarios by adapting a well-generalized model initialization to handle new tasks. Nonetheless, these appr... | 2022.acl-long.44 | 10.18653/v1/2022.acl-long.44 | null | 2203.11670 | title_snapshot |
2022.acl-long.45 | Quality Controlled Paraphrase Generation | https://aclanthology.org/2022.acl-long.45/ | [
"Elron Bandel",
"Ranit Aharonov",
"Michal Shmueli-Scheuer",
"Ilya Shnayderman",
"Noam Slonim",
"Liat Ein-Dor"
] | Paraphrase generation has been widely used in various downstream tasks. Most tasks benefit mainly from high quality paraphrases, namely those that are semantically similar to, yet linguistically diverse from, the original sentence. Generating high-quality paraphrases is challenging as it becomes increasingly hard to pr... | 2022.acl-long.45 | 10.18653/v1/2022.acl-long.45 | null | 2203.10940 | title_snapshot |
2022.acl-long.46 | Controllable Dictionary Example Generation: Generating Example Sentences for Specific Targeted Audiences | https://aclanthology.org/2022.acl-long.46/ | [
"Xingwei He",
"Siu Ming Yiu"
] | Example sentences for targeted words in a dictionary play an important role to help readers understand the usage of words. Traditionally, example sentences in a dictionary are usually created by linguistics experts, which are labor-intensive and knowledge-intensive. In this paper, we introduce the problem of dictionary... | 2022.acl-long.46 | 10.18653/v1/2022.acl-long.46 | null | null | null |
2022.acl-long.47 | AraT5: Text-to-Text Transformers for Arabic Language Generation | https://aclanthology.org/2022.acl-long.47/ | [
"El Moatez Billah Nagoudi",
"AbdelRahim Elmadany",
"Muhammad Abdul-Mageed"
] | Transfer learning with a unified Transformer framework (T5) that converts all language problems into a text-to-text format was recently proposed as a simple and effective transfer learning approach. Although a multilingual version of the T5 model (mT5) was also introduced, it is not clear how well it can fare on non-En... | 2022.acl-long.47 | 10.18653/v1/2022.acl-long.47 | null | 2109.12068 | title_snapshot |
2022.acl-long.48 | Legal Judgment Prediction via Event Extraction with Constraints | https://aclanthology.org/2022.acl-long.48/ | [
"Yi Feng",
"Chuanyi Li",
"Vincent Ng"
] | While significant progress has been made on the task of Legal Judgment Prediction (LJP) in recent years, the incorrect predictions made by SOTA LJP models can be attributed in part to their failure to (1) locate the key event information that determines the judgment, and (2) exploit the cross-task consistency constrain... | 2022.acl-long.48 | 10.18653/v1/2022.acl-long.48 | null | null | null |
2022.acl-long.49 | Answer-level Calibration for Free-form Multiple Choice Question Answering | https://aclanthology.org/2022.acl-long.49/ | [
"Sawan Kumar"
] | Pre-trained language models have recently shown that training on large corpora using the language modeling objective enables few-shot and zero-shot capabilities on a variety of NLP tasks, including commonsense reasoning tasks. This is achieved using text interactions with the model, usually by posing the task as a natu... | 2022.acl-long.49 | 10.18653/v1/2022.acl-long.49 | null | null | null |
2022.acl-long.50 | Learning When to Translate for Streaming Speech | https://aclanthology.org/2022.acl-long.50/ | [
"Qianqian Dong",
"Yaoming Zhu",
"Mingxuan Wang",
"Lei Li"
] | How to find proper moments to generate partial sentence translation given a streaming speech input? Existing approaches waiting-and-translating for a fixed duration often break the acoustic units in speech, since the boundaries between acoustic units in speech are not even. In this paper, we propose MoSST, a simple yet... | 2022.acl-long.50 | 10.18653/v1/2022.acl-long.50 | null | 2109.07368 | title_snapshot |
2022.acl-long.51 | Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking | https://aclanthology.org/2022.acl-long.51/ | [
"Yingrui Yang",
"Yifan Qiao",
"Tao Yang"
] | Transformer based re-ranking models can achieve high search relevance through context- aware soft matching of query tokens with document tokens. To alleviate runtime complexity of such inference, previous work has adopted a late interaction architecture with pre-computed contextual token representations at the cost of ... | 2022.acl-long.51 | 10.18653/v1/2022.acl-long.51 | null | 2203.15328 | title_snapshot |
2022.acl-long.52 | Early Stopping Based on Unlabeled Samples in Text Classification | https://aclanthology.org/2022.acl-long.52/ | [
"HongSeok Choi",
"Dongha Choi",
"Hyunju Lee"
] | Early stopping, which is widely used to prevent overfitting, is generally based on a separate validation set. However, in low resource settings, validation-based stopping can be risky because a small validation set may not be sufficiently representative, and the reduction in the number of samples by validation split ma... | 2022.acl-long.52 | 10.18653/v1/2022.acl-long.52 | null | null | null |
2022.acl-long.53 | Meta-learning via Language Model In-context Tuning | https://aclanthology.org/2022.acl-long.53/ | [
"Yanda Chen",
"Ruiqi Zhong",
"Sheng Zha",
"George Karypis",
"He He"
] | The goal of meta-learning is to learn to adapt to a new task with only a few labeled examples. Inspired by the recent progress in large language models, we propose \textit{in-context tuning} (ICT), which recasts task adaptation and prediction as a simple sequence prediction problem: to form the input sequence, we conca... | 2022.acl-long.53 | 10.18653/v1/2022.acl-long.53 | null | 2110.07814 | title_snapshot |
2022.acl-long.54 | It is AI’s Turn to Ask Humans a Question: Question-Answer Pair Generation for Children’s Story Books | https://aclanthology.org/2022.acl-long.54/ | [
"Bingsheng Yao",
"Dakuo Wang",
"Tongshuang Wu",
"Zheng Zhang",
"Toby Jia-Jun Li",
"Mo Yu",
"Ying Xu"
] | Existing question answering (QA) techniques are created mainly to answer questions asked by humans. But in educational applications, teachers often need to decide what questions they should ask, in order to help students to improve their narrative understanding capabilities. We design an automated question-answer gener... | 2022.acl-long.54 | 10.18653/v1/2022.acl-long.54 | null | 2109.03423 | title_snapshot |
2022.acl-long.55 | PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning | https://aclanthology.org/2022.acl-long.55/ | [
"Rongzhi Zhang",
"Yue Yu",
"Pranav Shetty",
"Le Song",
"Chao Zhang"
] | Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult. We study interactive weakly-supervised learning—the problem of iteratively and automatically discovering novel lab... | 2022.acl-long.55 | 10.18653/v1/2022.acl-long.55 | null | 2203.09735 | title_snapshot |
2022.acl-long.56 | Constrained Multi-Task Learning for Bridging Resolution | https://aclanthology.org/2022.acl-long.56/ | [
"Hideo Kobayashi",
"Yufang Hou",
"Vincent Ng"
] | We examine the extent to which supervised bridging resolvers can be improved without employing additional labeled bridging data by proposing a novel constrained multi-task learning framework for bridging resolution, within which we (1) design cross-task consistency constraints to guide the learning process; (2) pre-tra... | 2022.acl-long.56 | 10.18653/v1/2022.acl-long.56 | null | null | null |
2022.acl-long.57 | DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations | https://aclanthology.org/2022.acl-long.57/ | [
"Sarik Ghazarian",
"Nuan Wen",
"Aram Galstyan",
"Nanyun Peng"
] | Automatic evaluation metrics are essential for the rapid development of open-domain dialogue systems as they facilitate hyper-parameter tuning and comparison between models. Although recently proposed trainable conversation-level metrics have shown encouraging results, the quality of the metrics is strongly dependent o... | 2022.acl-long.57 | 10.18653/v1/2022.acl-long.57 | null | 2203.09711 | title_snapshot |
2022.acl-long.58 | HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarization | https://aclanthology.org/2022.acl-long.58/ | [
"Shuyang Cao",
"Lu Wang"
] | Document structure is critical for efficient information consumption. However, it is challenging to encode it efficiently into the modern Transformer architecture. In this work, we present HIBRIDS, which injects Hierarchical Biases foR Incorporating Document Structure into attention score calculation. We further presen... | 2022.acl-long.58 | 10.18653/v1/2022.acl-long.58 | null | 2203.10741 | title_snapshot |
2022.acl-long.59 | De-Bias for Generative Extraction in Unified NER Task | https://aclanthology.org/2022.acl-long.59/ | [
"Shuai Zhang",
"Yongliang Shen",
"Zeqi Tan",
"Yiquan Wu",
"Weiming Lu"
] | Named entity recognition (NER) is a fundamental task to recognize specific types of entities from a given sentence. Depending on how the entities appear in the sentence, it can be divided into three subtasks, namely, Flat NER, Nested NER, and Discontinuous NER. Among the existing approaches, only the generative model c... | 2022.acl-long.59 | 10.18653/v1/2022.acl-long.59 | null | null | null |
2022.acl-long.60 | An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels | https://aclanthology.org/2022.acl-long.60/ | [
"Taylor Sorensen",
"Joshua Robinson",
"Christopher Rytting",
"Alexander Shaw",
"Kyle Rogers",
"Alexia Delorey",
"Mahmoud Khalil",
"Nancy Fulda",
"David Wingate"
] | Pre-trained language models derive substantial linguistic and factual knowledge from the massive corpora on which they are trained, and prompt engineering seeks to align these models to specific tasks. Unfortunately, existing prompt engineering methods require significant amounts of labeled data, access to model parame... | 2022.acl-long.60 | 10.18653/v1/2022.acl-long.60 | null | 2203.11364 | title_snapshot |
2022.acl-long.61 | Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation | https://aclanthology.org/2022.acl-long.61/ | [
"Xinyi Wang",
"Sebastian Ruder",
"Graham Neubig"
] | The performance of multilingual pretrained models is highly dependent on the availability of monolingual or parallel text present in a target language. Thus, the majority of the world’s languages cannot benefit from recent progress in NLP as they have no or limited textual data. To expand possibilities of using NLP tec... | 2022.acl-long.61 | 10.18653/v1/2022.acl-long.61 | null | 2203.09435 | title_snapshot |
2022.acl-long.62 | Language-agnostic BERT Sentence Embedding | https://aclanthology.org/2022.acl-long.62/ | [
"Fangxiaoyu Feng",
"Yinfei Yang",
"Daniel Cer",
"Naveen Arivazhagan",
"Wei Wang"
] | While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning BERT based cross-lingual sentence embeddings have yet to be explored. We systematically investigate methods for learning multilingual sentence embeddings by combining the best met... | 2022.acl-long.62 | 10.18653/v1/2022.acl-long.62 | null | 2007.01852 | title_snapshot |
2022.acl-long.63 | Nested Named Entity Recognition with Span-level Graphs | https://aclanthology.org/2022.acl-long.63/ | [
"Juncheng Wan",
"Dongyu Ru",
"Weinan Zhang",
"Yong Yu"
] | Span-based methods with the neural networks backbone have great potential for the nested named entity recognition (NER) problem. However, they face problems such as degenerating when positive instances and negative instances largely overlap. Besides, the generalization ability matters a lot in nested NER, as a large pr... | 2022.acl-long.63 | 10.18653/v1/2022.acl-long.63 | null | null | null |
2022.acl-long.64 | CogTaskonomy: Cognitively Inspired Task Taxonomy Is Beneficial to Transfer Learning in NLP | https://aclanthology.org/2022.acl-long.64/ | [
"Yifei Luo",
"Minghui Xu",
"Deyi Xiong"
] | Is there a principle to guide transfer learning across tasks in natural language processing (NLP)? Taxonomy (Zamir et al., 2018) finds that a structure exists among visual tasks, as a principle underlying transfer learning for them. In this paper, we propose a cognitively inspired framework, CogTaskonomy, to learn taxo... | 2022.acl-long.64 | 10.18653/v1/2022.acl-long.64 | null | null | null |
2022.acl-long.65 | RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining | https://aclanthology.org/2022.acl-long.65/ | [
"Hui Su",
"Weiwei Shi",
"Xiaoyu Shen",
"Zhou Xiao",
"Tuo Ji",
"Jiarui Fang",
"Jie Zhou"
] | Large-scale pretrained language models have achieved SOTA results on NLP tasks. However, they have been shown vulnerable to adversarial attacks especially for logographic languages like Chinese. In this work, we propose RoCBert: a pretrained Chinese Bert that is robust to various forms of adversarial attacks like word ... | 2022.acl-long.65 | 10.18653/v1/2022.acl-long.65 | null | null | null |
2022.acl-long.66 | Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues | https://aclanthology.org/2022.acl-long.66/ | [
"Qingxiu Dong",
"Ziwei Qin",
"Heming Xia",
"Tian Feng",
"Shoujie Tong",
"Haoran Meng",
"Lin Xu",
"Zhongyu Wei",
"Weidong Zhan",
"Baobao Chang",
"Sujian Li",
"Tianyu Liu",
"Zhifang Sui"
] | It is a common practice for recent works in vision language cross-modal reasoning to adopt a binary or multi-choice classification formulation taking as input a set of source image(s) and textual query. In this work, we take a sober look at such an “unconditional” formulation in the sense that no prior knowledge is spe... | 2022.acl-long.66 | 10.18653/v1/2022.acl-long.66 | null | 2105.07122 | title_snapshot |
2022.acl-long.67 | Parallel Instance Query Network for Named Entity Recognition | https://aclanthology.org/2022.acl-long.67/ | [
"Yongliang Shen",
"Xiaobin Wang",
"Zeqi Tan",
"Guangwei Xu",
"Pengjun Xie",
"Fei Huang",
"Weiming Lu",
"Yueting Zhuang"
] | Named entity recognition (NER) is a fundamental task in natural language processing. Recent works treat named entity recognition as a reading comprehension task, constructing type-specific queries manually to extract entities. This paradigm suffers from three issues. First, type-specific queries can only extract one ty... | 2022.acl-long.67 | 10.18653/v1/2022.acl-long.67 | null | 2203.10545 | title_snapshot |
2022.acl-long.68 | ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation | https://aclanthology.org/2022.acl-long.68/ | [
"Chang Liu",
"Xu Tan",
"Chongyang Tao",
"Zhenxin Fu",
"Dongyan Zhao",
"Tie-Yan Liu",
"Rui Yan"
] | Typical generative dialogue models utilize the dialogue history to generate the response. However, since one dialogue utterance can often be appropriately answered by multiple distinct responses, generating a desired response solely based on the historical information is not easy. Intuitively, if the chatbot can forese... | 2022.acl-long.68 | 10.18653/v1/2022.acl-long.68 | null | null | null |
2022.acl-long.69 | Modeling Multi-hop Question Answering as Single Sequence Prediction | https://aclanthology.org/2022.acl-long.69/ | [
"Semih Yavuz",
"Kazuma Hashimoto",
"Yingbo Zhou",
"Nitish Shirish Keskar",
"Caiming Xiong"
] | Fusion-in-decoder (Fid) (Izacard and Grave, 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained transformer and pushed the state of the art on single-hop QA. However, the complexity of multi-hop QA hinders the effectiveness of the generative QA approach. In this work,... | 2022.acl-long.69 | 10.18653/v1/2022.acl-long.69 | null | 2205.09226 | title_snapshot |
2022.acl-long.70 | Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension | https://aclanthology.org/2022.acl-long.70/ | [
"Linjuan Wu",
"Shaojuan Wu",
"Xiaowang Zhang",
"Deyi Xiong",
"Shizhan Chen",
"Zhiqiang Zhuang",
"Zhiyong Feng"
] | Multilingual pre-trained models are able to zero-shot transfer knowledge from rich-resource to low-resource languages in machine reading comprehension (MRC). However, inherent linguistic discrepancies in different languages could make answer spans predicted by zero-shot transfer violate syntactic constraints of the tar... | 2022.acl-long.70 | 10.18653/v1/2022.acl-long.70 | null | 2204.00996 | title_snapshot |
2022.acl-long.71 | Multi-Granularity Structural Knowledge Distillation for Language Model Compression | https://aclanthology.org/2022.acl-long.71/ | [
"Chang Liu",
"Chongyang Tao",
"Jiazhan Feng",
"Dongyan Zhao"
] | Transferring the knowledge to a small model through distillation has raised great interest in recent years. Prevailing methods transfer the knowledge derived from mono-granularity language units (e.g., token-level or sample-level), which is not enough to represent the rich semantics of a text and may lose some vital kn... | 2022.acl-long.71 | 10.18653/v1/2022.acl-long.71 | null | null | null |
2022.acl-long.72 | Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts | https://aclanthology.org/2022.acl-long.72/ | [
"Yue Guo",
"Yi Yang",
"Ahmed Abbasi"
] | Human-like biases and undesired social stereotypes exist in large pretrained language models. Given the wide adoption of these models in real-world applications, mitigating such biases has become an emerging and important task. In this paper, we propose an automatic method to mitigate the biases in pretrained language ... | 2022.acl-long.72 | 10.18653/v1/2022.acl-long.72 | null | null | null |
2022.acl-long.73 | Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals | https://aclanthology.org/2022.acl-long.73/ | [
"Zeming Liu",
"Jun Xu",
"Zeyang Lei",
"Haifeng Wang",
"Zheng-Yu Niu",
"Hua Wu"
] | Most dialog systems posit that users have figured out clear and specific goals before starting an interaction. For example, users have determined the departure, the destination, and the travel time for booking a flight. However, in many scenarios, limited by experience and knowledge, users may know what they need, but ... | 2022.acl-long.73 | 10.18653/v1/2022.acl-long.73 | null | 2204.07299 | title_snapshot |
2022.acl-long.74 | Semi-supervised Domain Adaptation for Dependency Parsing with Dynamic Matching Network | https://aclanthology.org/2022.acl-long.74/ | [
"Ying Li",
"Shuaike Li",
"Min Zhang"
] | Supervised parsing models have achieved impressive results on in-domain texts. However, their performances drop drastically on out-of-domain texts due to the data distribution shift. The shared-private model has shown its promising advantages for alleviating this problem via feature separation, whereas prior works pay ... | 2022.acl-long.74 | 10.18653/v1/2022.acl-long.74 | null | null | null |
2022.acl-long.75 | A Closer Look at How Fine-tuning Changes BERT | https://aclanthology.org/2022.acl-long.75/ | [
"Yichu Zhou",
"Vivek Srikumar"
] | Given the prevalence of pre-trained contextualized representations in today’s NLP, there have been many efforts to understand what information they contain, and why they seem to be universally successful. The most common approach to use these representations involves fine-tuning them for an end task. Yet, how fine-tuni... | 2022.acl-long.75 | 10.18653/v1/2022.acl-long.75 | null | 2106.14282 | title_snapshot |
2022.acl-long.76 | Sentence-aware Contrastive Learning for Open-Domain Passage Retrieval | https://aclanthology.org/2022.acl-long.76/ | [
"Bohong Wu",
"Zhuosheng Zhang",
"Jinyuan Wang",
"Hai Zhao"
] | Training dense passage representations via contrastive learning has been shown effective for Open-Domain Passage Retrieval (ODPR). Existing studies focus on further optimizing by improving negative sampling strategy or extra pretraining. However, these studies keep unknown in capturing passage with internal representat... | 2022.acl-long.76 | 10.18653/v1/2022.acl-long.76 | null | 2110.07524 | title_snapshot |
2022.acl-long.77 | FaiRR: Faithful and Robust Deductive Reasoning over Natural Language | https://aclanthology.org/2022.acl-long.77/ | [
"Soumya Sanyal",
"Harman Singh",
"Xiang Ren"
] | Transformers have been shown to be able to perform deductive reasoning on a logical rulebase containing rules and statements written in natural language. Recent works show that such models can also produce the reasoning steps (i.e., the proof graph) that emulate the model’s logical reasoning process. Currently, these b... | 2022.acl-long.77 | 10.18653/v1/2022.acl-long.77 | null | 2203.10261 | title_snapshot |
2022.acl-long.78 | HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation | https://aclanthology.org/2022.acl-long.78/ | [
"Zhoujun Cheng",
"Haoyu Dong",
"Zhiruo Wang",
"Ran Jia",
"Jiaqi Guo",
"Yan Gao",
"Shi Han",
"Jian-Guang Lou",
"Dongmei Zhang"
] | Tables are often created with hierarchies, but existing works on table reasoning mainly focus on flat tables and neglect hierarchical tables. Hierarchical tables challenge numerical reasoning by complex hierarchical indexing, as well as implicit relationships of calculation and semantics. We present a new dataset, HiTa... | 2022.acl-long.78 | 10.18653/v1/2022.acl-long.78 | null | 2108.06712 | title_snapshot |
2022.acl-long.79 | Doctor Recommendation in Online Health Forums via Expertise Learning | https://aclanthology.org/2022.acl-long.79/ | [
"Xiaoxin Lu",
"Yubo Zhang",
"Jing Li",
"Shi Zong"
] | Huge volumes of patient queries are daily generated on online health forums, rendering manual doctor allocation a labor-intensive task. To better help patients, this paper studies a novel task of doctor recommendation to enable automatic pairing of a patient to a doctor with relevant expertise. While most prior work in... | 2022.acl-long.79 | 10.18653/v1/2022.acl-long.79 | null | 2203.02932 | title_snapshot |
2022.acl-long.80 | Continual Prompt Tuning for Dialog State Tracking | https://aclanthology.org/2022.acl-long.80/ | [
"Qi Zhu",
"Bing Li",
"Fei Mi",
"Xiaoyan Zhu",
"Minlie Huang"
] | A desirable dialog system should be able to continually learn new skills without forgetting old ones, and thereby adapt to new domains or tasks in its life cycle. However, continually training a model often leads to a well-known catastrophic forgetting issue. In this paper, we present Continual Prompt Tuning, a paramet... | 2022.acl-long.80 | 10.18653/v1/2022.acl-long.80 | null | 2203.06654 | title_snapshot |
2022.acl-long.81 | There’s a Time and Place for Reasoning Beyond the Image | https://aclanthology.org/2022.acl-long.81/ | [
"Xingyu Fu",
"Ben Zhou",
"Ishaan Chandratreya",
"Carl Vondrick",
"Dan Roth"
] | Images are often more significant than only the pixels to human eyes, as we can infer, associate, and reason with contextual information from other sources to establish a more complete picture. For example, in Figure 1, we can find a way to identify the news articles related to the picture through segment-wise understa... | 2022.acl-long.81 | 10.18653/v1/2022.acl-long.81 | null | null | null |
2022.acl-long.82 | FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining | https://aclanthology.org/2022.acl-long.82/ | [
"Zhoujun Cheng",
"Haoyu Dong",
"Ran Jia",
"Pengfei Wu",
"Shi Han",
"Fan Cheng",
"Dongmei Zhang"
] | Tables store rich numerical data, but numerical reasoning over tables is still a challenge. In this paper, we find that the spreadsheet formula, a commonly used language to perform computations on numerical values in spreadsheets, is a valuable supervision for numerical reasoning in tables. Considering large amounts of... | 2022.acl-long.82 | 10.18653/v1/2022.acl-long.82 | null | 2109.07323 | title_snapshot |
2022.acl-long.83 | Multimodal fusion via cortical network inspired losses | https://aclanthology.org/2022.acl-long.83/ | [
"Shiv Shankar"
] | Information integration from different modalities is an active area of research. Human beings and, in general, biological neural systems are quite adept at using a multitude of signals from different sensory perceptive fields to interact with the environment and each other. Recent work in deep fusion models via neural ... | 2022.acl-long.83 | 10.18653/v1/2022.acl-long.83 | null | null | null |
2022.acl-long.84 | Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension | https://aclanthology.org/2022.acl-long.84/ | [
"Huibin Zhang",
"Zhengkun Zhang",
"Yao Zhang",
"Jun Wang",
"Yufan Li",
"Ning Jiang",
"Xin Wei",
"Zhenglu Yang"
] | Procedural Multimodal Documents (PMDs) organize textual instructions and corresponding images step by step. Comprehending PMDs and inducing their representations for the downstream reasoning tasks is designated as Procedural MultiModal Machine Comprehension (M3C). In this study, we approach Procedural M3C at a fine-gra... | 2022.acl-long.84 | 10.18653/v1/2022.acl-long.84 | null | 2204.02566 | title_snapshot |
2022.acl-long.85 | Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning | https://aclanthology.org/2022.acl-long.85/ | [
"Swarnadeep Saha",
"Prateek Yadav",
"Mohit Bansal"
] | Pre-trained sequence-to-sequence language models have led to widespread success in many natural language generation tasks. However, there has been relatively less work on analyzing their ability to generate structured outputs such as graphs. Unlike natural language, graphs have distinct structural and semantic properti... | 2022.acl-long.85 | 10.18653/v1/2022.acl-long.85 | null | 2204.04813 | title_snapshot |
2022.acl-long.86 | Unsupervised Extractive Opinion Summarization Using Sparse Coding | https://aclanthology.org/2022.acl-long.86/ | [
"Somnath Basu Roy Chowdhury",
"Chao Zhao",
"Snigdha Chaturvedi"
] | Opinion summarization is the task of automatically generating summaries that encapsulate information expressed in multiple user reviews. We present Semantic Autoencoder (SemAE) to perform extractive opinion summarization in an unsupervised manner. SemAE uses dictionary learning to implicitly capture semantic informatio... | 2022.acl-long.86 | 10.18653/v1/2022.acl-long.86 | null | 2203.07921 | title_snapshot |
2022.acl-long.87 | LexSubCon: Integrating Knowledge from Lexical Resources into Contextual Embeddings for Lexical Substitution | https://aclanthology.org/2022.acl-long.87/ | [
"George Michalopoulos",
"Ian McKillop",
"Alexander Wong",
"Helen Chen"
] | Lexical substitution is the task of generating meaningful substitutes for a word in a given textual context. Contextual word embedding models have achieved state-of-the-art results in the lexical substitution task by relying on contextual information extracted from the replaced word within the sentence. However, such m... | 2022.acl-long.87 | 10.18653/v1/2022.acl-long.87 | null | 2107.05132 | title_snapshot |
2022.acl-long.88 | Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation | https://aclanthology.org/2022.acl-long.88/ | [
"Pei Zhou",
"Karthik Gopalakrishnan",
"Behnam Hedayatnia",
"Seokhwan Kim",
"Jay Pujara",
"Xiang Ren",
"Yang Liu",
"Dilek Hakkani-Tur"
] | Implicit knowledge, such as common sense, is key to fluid human conversations. Current neural response generation (RG) models are trained to generate responses directly, omitting unstated implicit knowledge. In this paper, we present Think-Before-Speaking (TBS), a generative approach to first externalize implicit commo... | 2022.acl-long.88 | 10.18653/v1/2022.acl-long.88 | null | 2110.08501 | title_snapshot |
2022.acl-long.89 | Flow-Adapter Architecture for Unsupervised Machine Translation | https://aclanthology.org/2022.acl-long.89/ | [
"Yihong Liu",
"Haris Jabbar",
"Hinrich Schuetze"
] | In this work, we propose a flow-adapter architecture for unsupervised NMT. It leverages normalizing flows to explicitly model the distributions of sentence-level latent representations, which are subsequently used in conjunction with the attention mechanism for the translation task. The primary novelties of our model a... | 2022.acl-long.89 | 10.18653/v1/2022.acl-long.89 | null | 2204.12225 | title_snapshot |
2022.acl-long.90 | Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning | https://aclanthology.org/2022.acl-long.90/ | [
"Demian Ghalandari",
"Chris Hokamp",
"Georgiana Ifrim"
] | Sentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality. Unsupervised objective driven methods for sentence compression can be used to create customized models without the need for ground-truth training data, while allowing flexibility in the... | 2022.acl-long.90 | 10.18653/v1/2022.acl-long.90 | null | 2205.08221 | title_snapshot |
2022.acl-long.91 | Tracing Origins: Coreference-aware Machine Reading Comprehension | https://aclanthology.org/2022.acl-long.91/ | [
"Baorong Huang",
"Zhuosheng Zhang",
"Hai Zhao"
] | Machine reading comprehension is a heavily-studied research and test field for evaluating new pre-trained language models (PrLMs) and fine-tuning strategies, and recent studies have enriched the pre-trained language models with syntactic, semantic and other linguistic information to improve the performance of the model... | 2022.acl-long.91 | 10.18653/v1/2022.acl-long.91 | null | 2110.07961 | title_snapshot |
2022.acl-long.92 | WatClaimCheck: A new Dataset for Claim Entailment and Inference | https://aclanthology.org/2022.acl-long.92/ | [
"Kashif Khan",
"Ruizhe Wang",
"Pascal Poupart"
] | We contribute a new dataset for the task of automated fact checking and an evaluation of state of the art algorithms. The dataset includes claims (from speeches, interviews, social media and news articles), review articles published by professional fact checkers and premise articles used by those professional fact chec... | 2022.acl-long.92 | 10.18653/v1/2022.acl-long.92 | null | null | null |
2022.acl-long.93 | FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation | https://aclanthology.org/2022.acl-long.93/ | [
"Moussa Kamal Eddine",
"Guokan Shang",
"Antoine Tixier",
"Michalis Vazirgiannis"
] | Fast and reliable evaluation metrics are key to R&D progress. While traditional natural language generation metrics are fast, they are not very reliable. Conversely, new metrics based on large pretrained language models are much more reliable, but require significant computational resources. In this paper, we propose F... | 2022.acl-long.93 | 10.18653/v1/2022.acl-long.93 | null | 2110.08559 | title_judge |
2022.acl-long.94 | A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation | https://aclanthology.org/2022.acl-long.94/ | [
"Shashi Narayan",
"Gonçalo Simões",
"Yao Zhao",
"Joshua Maynez",
"Dipanjan Das",
"Michael Collins",
"Mirella Lapata"
] | We propose Composition Sampling, a simple but effective method to generate diverse outputs for conditional generation of higher quality compared to previous stochastic decoding strategies. It builds on recently proposed plan-based neural generation models (FROST, Narayan et al, 2021) that are trained to first create a ... | 2022.acl-long.94 | 10.18653/v1/2022.acl-long.94 | null | 2203.15108 | title_snapshot |
2022.acl-long.95 | Synthetic Question Value Estimation for Domain Adaptation of Question Answering | https://aclanthology.org/2022.acl-long.95/ | [
"Xiang Yue",
"Ziyu Yao",
"Huan Sun"
] | Synthesizing QA pairs with a question generator (QG) on the target domain has become a popular approach for domain adaptation of question answering (QA) models. Since synthetic questions are often noisy in practice, existing work adapts scores from a pretrained QA (or QG) model as criteria to select high-quality questi... | 2022.acl-long.95 | 10.18653/v1/2022.acl-long.95 | null | 2203.08926 | title_snapshot |
2022.acl-long.96 | Better Language Model with Hypernym Class Prediction | https://aclanthology.org/2022.acl-long.96/ | [
"He Bai",
"Tong Wang",
"Alessandro Sordoni",
"Peng Shi"
] | Class-based language models (LMs) have been long devised to address context sparsity in n-gram LMs. In this study, we revisit this approach in the context of neural LMs. We hypothesize that class-based prediction leads to an implicit context aggregation for similar words and thus can improve generalization for rare wor... | 2022.acl-long.96 | 10.18653/v1/2022.acl-long.96 | null | 2203.10692 | title_snapshot |
2022.acl-long.97 | Tackling Fake News Detection by Continually Improving Social Context Representations using Graph Neural Networks | https://aclanthology.org/2022.acl-long.97/ | [
"Nikhil Mehta",
"Maria Leonor Pacheco",
"Dan Goldwasser"
] | Easy access, variety of content, and fast widespread interactions are some of the reasons making social media increasingly popular. However, this rise has also enabled the propagation of fake news, text published by news sources with an intent to spread misinformation and sway beliefs. Detecting it is an important and ... | 2022.acl-long.97 | 10.18653/v1/2022.acl-long.97 | null | null | null |
2022.acl-long.98 | Understanding Gender Bias in Knowledge Base Embeddings | https://aclanthology.org/2022.acl-long.98/ | [
"Yupei Du",
"Qi Zheng",
"Yuanbin Wu",
"Man Lan",
"Yan Yang",
"Meirong Ma"
] | Knowledge base (KB) embeddings have been shown to contain gender biases. In this paper, we study two questions regarding these biases: how to quantify them, and how to trace their origins in KB? Specifically, first, we develop two novel bias measures respectively for a group of person entities and an individual person ... | 2022.acl-long.98 | 10.18653/v1/2022.acl-long.98 | null | null | null |
2022.acl-long.99 | Computational Historical Linguistics and Language Diversity in South Asia | https://aclanthology.org/2022.acl-long.99/ | [
"Aryaman Arora",
"Adam Farris",
"Samopriya Basu",
"Suresh Kolichala"
] | South Asia is home to a plethora of languages, many of which severely lack access to new language technologies. This linguistic diversity also results in a research environment conducive to the study of comparative, contact, and historical linguistics–fields which necessitate the gathering of extensive data from many l... | 2022.acl-long.99 | 10.18653/v1/2022.acl-long.99 | null | 2203.12524 | title_snapshot |
2022.acl-long.100 | Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization | https://aclanthology.org/2022.acl-long.100/ | [
"Faisal Ladhak",
"Esin Durmus",
"He He",
"Claire Cardie",
"Kathleen McKeown"
] | Despite recent progress in abstractive summarization, systems still suffer from faithfulness errors. While prior work has proposed models that improve faithfulness, it is unclear whether the improvement comes from an increased level of extractiveness of the model outputs as one naive way to improve faithfulness is to m... | 2022.acl-long.100 | 10.18653/v1/2022.acl-long.100 | null | 2108.13684 | title_snapshot |
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