paper_id stringlengths 15 18 | title stringlengths 22 143 | paper_url stringlengths 41 44 | authors listlengths 1 16 | abstract large_stringlengths 439 1.79k | anthology_id stringlengths 15 18 | doi stringlengths 27 30 | award stringclasses 3
values | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|
2021.acl-long.301 | A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections | https://aclanthology.org/2021.acl-long.301/ | [
"Dimitris Pappas",
"Ion Androutsopoulos"
] | Question answering (QA) systems for large document collections typically use pipelines that (i) retrieve possibly relevant documents, (ii) re-rank them, (iii) rank paragraphs or other snippets of the top-ranked documents, and (iv) select spans of the top-ranked snippets as exact answers. Pipelines are conceptually simp... | 2021.acl-long.301 | 10.18653/v1/2021.acl-long.301 | null | 2106.08908 | title_snapshot |
2021.acl-long.302 | W-RST: Towards a Weighted RST-style Discourse Framework | https://aclanthology.org/2021.acl-long.302/ | [
"Patrick Huber",
"Wen Xiao",
"Giuseppe Carenini"
] | Aiming for a better integration of data-driven and linguistically-inspired approaches, we explore whether RST Nuclearity, assigning a binary assessment of importance between text segments, can be replaced by automatically generated, real-valued scores, in what we call a Weighted-RST framework. In particular, we find th... | 2021.acl-long.302 | 10.18653/v1/2021.acl-long.302 | null | 2106.02658 | title_snapshot |
2021.acl-long.303 | ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences | https://aclanthology.org/2021.acl-long.303/ | [
"Yanjun Gao",
"Ting-Hao Huang",
"Rebecca J. Passonneau"
] | Atomic clauses are fundamental text units for understanding complex sentences. Identifying the atomic sentences within complex sentences is important for applications such as summarization, argument mining, discourse analysis, discourse parsing, and question answering. Previous work mainly relies on rule-based methods ... | 2021.acl-long.303 | 10.18653/v1/2021.acl-long.303 | null | 2106.12027 | title_snapshot |
2021.acl-long.304 | Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering | https://aclanthology.org/2021.acl-long.304/ | [
"Najoung Kim",
"Ellie Pavlick",
"Burcu Karagol Ayan",
"Deepak Ramachandran"
] | Many Question-Answering (QA) datasets contain unanswerable questions, but their treatment in QA systems remains primitive. Our analysis of the Natural Questions (Kwiatkowski et al. 2019) dataset reveals that a substantial portion of unanswerable questions (~21%) can be explained based on the presence of unverifiable pr... | 2021.acl-long.304 | 10.18653/v1/2021.acl-long.304 | null | 2101.00391 | title_snapshot |
2021.acl-long.305 | Adversarial Learning for Discourse Rhetorical Structure Parsing | https://aclanthology.org/2021.acl-long.305/ | [
"Longyin Zhang",
"Fang Kong",
"Guodong Zhou"
] | Text-level discourse rhetorical structure (DRS) parsing is known to be challenging due to the notorious lack of training data. Although recent top-down DRS parsers can better leverage global document context and have achieved certain success, the performance is still far from perfect. To our knowledge, all previous DRS... | 2021.acl-long.305 | 10.18653/v1/2021.acl-long.305 | null | null | null |
2021.acl-long.306 | Exploring Discourse Structures for Argument Impact Classification | https://aclanthology.org/2021.acl-long.306/ | [
"Xin Liu",
"Jiefu Ou",
"Yangqiu Song",
"Xin Jiang"
] | Discourse relations among arguments reveal logical structures of a debate conversation. However, no prior work has explicitly studied how the sequence of discourse relations influence a claim’s impact. This paper empirically shows that the discourse relations between two arguments along the context path are essential f... | 2021.acl-long.306 | 10.18653/v1/2021.acl-long.306 | null | 2106.00976 | title_snapshot |
2021.acl-long.307 | Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation | https://aclanthology.org/2021.acl-long.307/ | [
"Tong Zhang",
"Long Zhang",
"Wei Ye",
"Bo Li",
"Jinan Sun",
"Xiaoyu Zhu",
"Wen Zhao",
"Shikun Zhang"
] | This paper proposes a sophisticated neural architecture to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models. By introducing three novel components: Pointer, Disambiguator, and Copier, our method PDC achieves the following merits inherently compared with previous efforts: (1) Pointer lever... | 2021.acl-long.307 | 10.18653/v1/2021.acl-long.307 | null | null | null |
2021.acl-long.308 | VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation | https://aclanthology.org/2021.acl-long.308/ | [
"Fuli Luo",
"Wei Wang",
"Jiahao Liu",
"Yijia Liu",
"Bin Bi",
"Songfang Huang",
"Fei Huang",
"Luo Si"
] | Existing work in multilingual pretraining has demonstrated the potential of cross-lingual transferability by training a unified Transformer encoder for multiple languages. However, much of this work only relies on the shared vocabulary and bilingual contexts to encourage the correlation across languages, which is loose... | 2021.acl-long.308 | 10.18653/v1/2021.acl-long.308 | null | 2010.16046 | title_snapshot |
2021.acl-long.309 | A unified approach to sentence segmentation of punctuated text in many languages | https://aclanthology.org/2021.acl-long.309/ | [
"Rachel Wicks",
"Matt Post"
] | The sentence is a fundamental unit of text processing. Yet sentences in the wild are commonly encountered not in isolation, but unsegmented within larger paragraphs and documents. Therefore, the first step in many NLP pipelines is sentence segmentation. Despite its importance, this step is the subject of relatively lit... | 2021.acl-long.309 | 10.18653/v1/2021.acl-long.309 | null | null | null |
2021.acl-long.310 | Towards User-Driven Neural Machine Translation | https://aclanthology.org/2021.acl-long.310/ | [
"Huan Lin",
"Liang Yao",
"Baosong Yang",
"Dayiheng Liu",
"Haibo Zhang",
"Weihua Luo",
"Degen Huang",
"Jinsong Su"
] | A good translation should not only translate the original content semantically, but also incarnate personal traits of the original text. For a real-world neural machine translation (NMT) system, these user traits (e.g., topic preference, stylistic characteristics and expression habits) can be preserved in user behavior... | 2021.acl-long.310 | 10.18653/v1/2021.acl-long.310 | null | 2106.06200 | title_snapshot |
2021.acl-long.311 | End-to-End Lexically Constrained Machine Translation for Morphologically Rich Languages | https://aclanthology.org/2021.acl-long.311/ | [
"Josef Jon",
"João Paulo Aires",
"Dusan Varis",
"Ondřej Bojar"
] | Lexically constrained machine translation allows the user to manipulate the output sentence by enforcing the presence or absence of certain words and phrases. Although current approaches can enforce terms to appear in the translation, they often struggle to make the constraint word form agree with the rest of the gener... | 2021.acl-long.311 | 10.18653/v1/2021.acl-long.311 | null | 2106.12398 | title_snapshot |
2021.acl-long.312 | Handling Extreme Class Imbalance in Technical Logbook Datasets | https://aclanthology.org/2021.acl-long.312/ | [
"Farhad Akhbardeh",
"Cecilia Ovesdotter Alm",
"Marcos Zampieri",
"Travis Desell"
] | Technical logbooks are a challenging and under-explored text type in automated event identification. These texts are typically short and written in non-standard yet technical language, posing challenges to off-the-shelf NLP pipelines. The granularity of issue types described in these datasets additionally leads to clas... | 2021.acl-long.312 | 10.18653/v1/2021.acl-long.312 | null | null | null |
2021.acl-long.313 | ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation | https://aclanthology.org/2021.acl-long.313/ | [
"Vijit Malik",
"Rishabh Sanjay",
"Shubham Kumar Nigam",
"Kripabandhu Ghosh",
"Shouvik Kumar Guha",
"Arnab Bhattacharya",
"Ashutosh Modi"
] | An automated system that could assist a judge in predicting the outcome of a case would help expedite the judicial process. For such a system to be practically useful, predictions by the system should be explainable. To promote research in developing such a system, we introduce ILDC (Indian Legal Documents Corpus). ILD... | 2021.acl-long.313 | 10.18653/v1/2021.acl-long.313 | null | 2105.13562 | title_snapshot |
2021.acl-long.314 | Supporting Cognitive and Emotional Empathic Writing of Students | https://aclanthology.org/2021.acl-long.314/ | [
"Thiemo Wambsganss",
"Christina Niklaus",
"Matthias Söllner",
"Siegfried Handschuh",
"Jan Marco Leimeister"
] | We present an annotation approach to capturing emotional and cognitive empathy in student-written peer reviews on business models in German. We propose an annotation scheme that allows us to model emotional and cognitive empathy scores based on three types of review components. Also, we conducted an annotation study wi... | 2021.acl-long.314 | 10.18653/v1/2021.acl-long.314 | null | 2105.14815 | title_snapshot |
2021.acl-long.315 | Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering | https://aclanthology.org/2021.acl-long.315/ | [
"Alexander Hanbo Li",
"Patrick Ng",
"Peng Xu",
"Henghui Zhu",
"Zhiguo Wang",
"Bing Xiang"
] | The current state-of-the-art generative models for open-domain question answering (ODQA) have focused on generating direct answers from unstructured textual information. However, a large amount of world’s knowledge is stored in structured databases, and need to be accessed using query languages such as SQL. Furthermore... | 2021.acl-long.315 | 10.18653/v1/2021.acl-long.315 | null | 2108.02866 | title_snapshot |
2021.acl-long.316 | Generation-Augmented Retrieval for Open-Domain Question Answering | https://aclanthology.org/2021.acl-long.316/ | [
"Yuning Mao",
"Pengcheng He",
"Xiaodong Liu",
"Yelong Shen",
"Jianfeng Gao",
"Jiawei Han",
"Weizhu Chen"
] | We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation of heuristically discovered relevant contexts without external resources as supervision. We demonstrate that the generated contexts substantially enrich the semantics of the queries and GA... | 2021.acl-long.316 | 10.18653/v1/2021.acl-long.316 | null | 2009.08553 | title_snapshot |
2021.acl-long.317 | Check It Again:Progressive Visual Question Answering via Visual Entailment | https://aclanthology.org/2021.acl-long.317/ | [
"Qingyi Si",
"Zheng Lin",
"Ming yu Zheng",
"Peng Fu",
"Weiping Wang"
] | While sophisticated neural-based models have achieved remarkable success in Visual Question Answering (VQA), these models tend to answer questions only according to superficial correlations between question and answer. Several recent approaches have been developed to address this language priors problem. However, most ... | 2021.acl-long.317 | 10.18653/v1/2021.acl-long.317 | null | 2106.04605 | title_snapshot |
2021.acl-long.318 | A Mutual Information Maximization Approach for the Spurious Solution Problem in Weakly Supervised Question Answering | https://aclanthology.org/2021.acl-long.318/ | [
"Zhihong Shao",
"Lifeng Shang",
"Qun Liu",
"Minlie Huang"
] | Weakly supervised question answering usually has only the final answers as supervision signals while the correct solutions to derive the answers are not provided. This setting gives rise to the spurious solution problem: there may exist many spurious solutions that coincidentally derive the correct answer, but training... | 2021.acl-long.318 | 10.18653/v1/2021.acl-long.318 | null | 2106.07174 | title_snapshot |
2021.acl-long.319 | Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy? | https://aclanthology.org/2021.acl-long.319/ | [
"Abhilasha Ravichander",
"Alan W Black",
"Thomas Norton",
"Shomir Wilson",
"Norman Sadeh"
] | Privacy plays a crucial role in preserving democratic ideals and personal autonomy. The dominant legal approach to privacy in many jurisdictions is the “Notice and Choice” paradigm, where privacy policies are the primary instrument used to convey information to users. However, privacy policies are long and complex docu... | 2021.acl-long.319 | 10.18653/v1/2021.acl-long.319 | null | null | null |
2021.acl-long.320 | Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning | https://aclanthology.org/2021.acl-long.320/ | [
"Christopher Schröder",
"Kim Bürgl",
"Yves Annanias",
"Andreas Niekler",
"Lydia Müller",
"Daniel Wiegreffe",
"Christian Bender",
"Christoph Mengs",
"Gerik Scheuermann",
"Gerhard Heyer"
] | Open pit mines left many regions worldwide inhospitable or uninhabitable. Many sites are left behind in a hazardous or contaminated state, show remnants of waste, or have other restrictions imposed upon them, e.g., for the protection of human or nature. Such information has to be permanently managed in order to reuse t... | 2021.acl-long.320 | 10.18653/v1/2021.acl-long.320 | null | 2105.05557 | title_snapshot |
2021.acl-long.321 | Reliability Testing for Natural Language Processing Systems | https://aclanthology.org/2021.acl-long.321/ | [
"Samson Tan",
"Shafiq Joty",
"Kathy Baxter",
"Araz Taeihagh",
"Gregory A. Bennett",
"Min-Yen Kan"
] | Questions of fairness, robustness, and transparency are paramount to address before deploying NLP systems. Central to these concerns is the question of reliability: Can NLP systems reliably treat different demographics fairly and function correctly in diverse and noisy environments? To address this, we argue for the ne... | 2021.acl-long.321 | 10.18653/v1/2021.acl-long.321 | null | 2105.02590 | title_snapshot |
2021.acl-long.322 | Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data | https://aclanthology.org/2021.acl-long.322/ | [
"Paul Pu Liang",
"Terrance Liu",
"Anna Cai",
"Michal Muszynski",
"Ryo Ishii",
"Nick Allen",
"Randy Auerbach",
"David Brent",
"Ruslan Salakhutdinov",
"Louis-Philippe Morency"
] | Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care. The ability to accurately and efficiently predict mood from easily collectible data has several important implications for the early detection, intervention, and treatment of mental health disorders. One promis... | 2021.acl-long.322 | 10.18653/v1/2021.acl-long.322 | null | 2106.13213 | title_snapshot |
2021.acl-long.323 | Anonymisation Models for Text Data: State of the art, Challenges and Future Directions | https://aclanthology.org/2021.acl-long.323/ | [
"Pierre Lison",
"Ildikó Pilán",
"David Sanchez",
"Montserrat Batet",
"Lilja Øvrelid"
] | This position paper investigates the problem of automated text anonymisation, which is a prerequisite for secure sharing of documents containing sensitive information about individuals. We summarise the key concepts behind text anonymisation and provide a review of current approaches. Anonymisation methods have so far ... | 2021.acl-long.323 | 10.18653/v1/2021.acl-long.323 | null | null | null |
2021.acl-long.324 | End-to-End AMR Coreference Resolution | https://aclanthology.org/2021.acl-long.324/ | [
"Qiankun Fu",
"Linfeng Song",
"Wenyu Du",
"Yue Zhang"
] | Although parsing to Abstract Meaning Representation (AMR) has become very popular and AMR has been shown effective on the many sentence-level downstream tasks, little work has studied how to generate AMRs that can represent multi-sentence information. We introduce the first end-to-end AMR coreference resolution model i... | 2021.acl-long.324 | 10.18653/v1/2021.acl-long.324 | null | null | null |
2021.acl-long.325 | How is BERT surprised? Layerwise detection of linguistic anomalies | https://aclanthology.org/2021.acl-long.325/ | [
"Bai Li",
"Zining Zhu",
"Guillaume Thomas",
"Yang Xu",
"Frank Rudzicz"
] | Transformer language models have shown remarkable ability in detecting when a word is anomalous in context, but likelihood scores offer no information about the cause of the anomaly. In this work, we use Gaussian models for density estimation at intermediate layers of three language models (BERT, RoBERTa, and XLNet), a... | 2021.acl-long.325 | 10.18653/v1/2021.acl-long.325 | null | 2105.07452 | title_snapshot |
2021.acl-long.326 | Psycholinguistic Tripartite Graph Network for Personality Detection | https://aclanthology.org/2021.acl-long.326/ | [
"Tao Yang",
"Feifan Yang",
"Haolan Ouyang",
"Xiaojun Quan"
] | Most of the recent work on personality detection from online posts adopts multifarious deep neural networks to represent the posts and builds predictive models in a data-driven manner, without the exploitation of psycholinguistic knowledge that may unveil the connections between one’s language use and his psychological... | 2021.acl-long.326 | 10.18653/v1/2021.acl-long.326 | null | 2106.04963 | title_snapshot |
2021.acl-long.327 | Verb Metaphor Detection via Contextual Relation Learning | https://aclanthology.org/2021.acl-long.327/ | [
"Wei Song",
"Shuhui Zhou",
"Ruiji Fu",
"Ting Liu",
"Lizhen Liu"
] | Correct natural language understanding requires computers to distinguish the literal and metaphorical senses of a word. Recent neu- ral models achieve progress on verb metaphor detection by viewing it as sequence labeling. In this paper, we argue that it is appropriate to view this task as relation classification betwe... | 2021.acl-long.327 | 10.18653/v1/2021.acl-long.327 | null | null | null |
2021.acl-long.328 | Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task | https://aclanthology.org/2021.acl-long.328/ | [
"Yun Tang",
"Juan Pino",
"Xian Li",
"Changhan Wang",
"Dmitriy Genzel"
] | Pretraining and multitask learning are widely used to improve the speech translation performance. In this study, we are interested in training a speech translation model along with an auxiliary text translation task. We conduct a detailed analysis to understand the impact of the auxiliary task on the primary task withi... | 2021.acl-long.328 | 10.18653/v1/2021.acl-long.328 | null | 2107.05782 | title_snapshot |
2021.acl-long.329 | Probing Toxic Content in Large Pre-Trained Language Models | https://aclanthology.org/2021.acl-long.329/ | [
"Nedjma Ousidhoum",
"Xinran Zhao",
"Tianqing Fang",
"Yangqiu Song",
"Dit-Yan Yeung"
] | Large pre-trained language models (PTLMs) have been shown to carry biases towards different social groups which leads to the reproduction of stereotypical and toxic content by major NLP systems. We propose a method based on logistic regression classifiers to probe English, French, and Arabic PTLMs and quantify the pote... | 2021.acl-long.329 | 10.18653/v1/2021.acl-long.329 | null | null | null |
2021.acl-long.330 | Societal Biases in Language Generation: Progress and Challenges | https://aclanthology.org/2021.acl-long.330/ | [
"Emily Sheng",
"Kai-Wei Chang",
"Prem Natarajan",
"Nanyun Peng"
] | Technology for language generation has advanced rapidly, spurred by advancements in pre-training large models on massive amounts of data and the need for intelligent agents to communicate in a natural manner. While techniques can effectively generate fluent text, they can also produce undesirable societal biases that c... | 2021.acl-long.330 | 10.18653/v1/2021.acl-long.330 | null | 2105.04054 | title_snapshot |
2021.acl-long.331 | Reservoir Transformers | https://aclanthology.org/2021.acl-long.331/ | [
"Sheng Shen",
"Alexei Baevski",
"Ari Morcos",
"Kurt Keutzer",
"Michael Auli",
"Douwe Kiela"
] | We demonstrate that transformers obtain impressive performance even when some of the layers are randomly initialized and never updated. Inspired by old and well-established ideas in machine learning, we explore a variety of non-linear “reservoir” layers interspersed with regular transformer layers, and show improvement... | 2021.acl-long.331 | 10.18653/v1/2021.acl-long.331 | null | 2012.15045 | title_snapshot |
2021.acl-long.332 | Subsequence Based Deep Active Learning for Named Entity Recognition | https://aclanthology.org/2021.acl-long.332/ | [
"Puria Radmard",
"Yassir Fathullah",
"Aldo Lipani"
] | Active Learning (AL) has been successfully applied to Deep Learning in order to drastically reduce the amount of data required to achieve high performance. Previous works have shown that lightweight architectures for Named Entity Recognition (NER) can achieve optimal performance with only 25% of the original training d... | 2021.acl-long.332 | 10.18653/v1/2021.acl-long.332 | null | null | null |
2021.acl-long.333 | Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models | https://aclanthology.org/2021.acl-long.333/ | [
"Tyler A. Chang",
"Yifan Xu",
"Weijian Xu",
"Zhuowen Tu"
] | In this paper, we detail the relationship between convolutions and self-attention in natural language tasks. We show that relative position embeddings in self-attention layers are equivalent to recently-proposed dynamic lightweight convolutions, and we consider multiple new ways of integrating convolutions into Transfo... | 2021.acl-long.333 | 10.18653/v1/2021.acl-long.333 | null | 2106.05505 | title_snapshot |
2021.acl-long.334 | BinaryBERT: Pushing the Limit of BERT Quantization | https://aclanthology.org/2021.acl-long.334/ | [
"Haoli Bai",
"Wei Zhang",
"Lu Hou",
"Lifeng Shang",
"Jin Jin",
"Xin Jiang",
"Qun Liu",
"Michael Lyu",
"Irwin King"
] | The rapid development of large pre-trained language models has greatly increased the demand for model compression techniques, among which quantization is a popular solution. In this paper, we propose BinaryBERT, which pushes BERT quantization to the limit by weight binarization. We find that a binary BERT is hard to be... | 2021.acl-long.334 | 10.18653/v1/2021.acl-long.334 | null | 2012.15701 | title_snapshot |
2021.acl-long.335 | Are Pretrained Convolutions Better than Pretrained Transformers? | https://aclanthology.org/2021.acl-long.335/ | [
"Yi Tay",
"Mostafa Dehghani",
"Jai Prakash Gupta",
"Vamsi Aribandi",
"Dara Bahri",
"Zhen Qin",
"Donald Metzler"
] | In the era of pre-trained language models, Transformers are the de facto choice of model architectures. While recent research has shown promise in entirely convolutional, or CNN, architectures, they have not been explored using the pre-train-fine-tune paradigm. In the context of language models, are convolutional model... | 2021.acl-long.335 | 10.18653/v1/2021.acl-long.335 | null | null | null |
2021.acl-long.336 | PairRE: Knowledge Graph Embeddings via Paired Relation Vectors | https://aclanthology.org/2021.acl-long.336/ | [
"Linlin Chao",
"Jianshan He",
"Taifeng Wang",
"Wei Chu"
] | Distance based knowledge graph embedding methods show promising results on link prediction task, on which two topics have been widely studied: one is the ability to handle complex relations, such as N-to-1, 1-to-N and N-to-N, the other is to encode various relation patterns, such as symmetry/antisymmetry. However, the ... | 2021.acl-long.336 | 10.18653/v1/2021.acl-long.336 | null | 2011.03798 | title_snapshot |
2021.acl-long.337 | Hierarchy-aware Label Semantics Matching Network for Hierarchical Text Classification | https://aclanthology.org/2021.acl-long.337/ | [
"Haibin Chen",
"Qianli Ma",
"Zhenxi Lin",
"Jiangyue Yan"
] | Hierarchical text classification is an important yet challenging task due to the complex structure of the label hierarchy. Existing methods ignore the semantic relationship between text and labels, so they cannot make full use of the hierarchical information. To this end, we formulate the text-label semantics relations... | 2021.acl-long.337 | 10.18653/v1/2021.acl-long.337 | null | null | null |
2021.acl-long.338 | HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalizability | https://aclanthology.org/2021.acl-long.338/ | [
"Jiaao Chen",
"Dinghan Shen",
"Weizhu Chen",
"Diyi Yang"
] | Fine-tuning large pre-trained models with task-specific data has achieved great success in NLP. However, it has been demonstrated that the majority of information within the self-attention networks is redundant and not utilized effectively during the fine-tuning stage. This leads to inferior results when generalizing t... | 2021.acl-long.338 | 10.18653/v1/2021.acl-long.338 | null | 2106.00149 | title_judge |
2021.acl-long.339 | Neural Stylistic Response Generation with Disentangled Latent Variables | https://aclanthology.org/2021.acl-long.339/ | [
"Qingfu Zhu",
"Wei-Nan Zhang",
"Ting Liu",
"William Yang Wang"
] | Generating open-domain conversational responses in the desired style usually suffers from the lack of parallel data in the style. Meanwhile, using monolingual stylistic data to increase style intensity often leads to the expense of decreasing content relevance. In this paper, we propose to disentangle the content and s... | 2021.acl-long.339 | 10.18653/v1/2021.acl-long.339 | null | null | null |
2021.acl-long.340 | Intent Classification and Slot Filling for Privacy Policies | https://aclanthology.org/2021.acl-long.340/ | [
"Wasi Ahmad",
"Jianfeng Chi",
"Tu Le",
"Thomas Norton",
"Yuan Tian",
"Kai-Wei Chang"
] | Understanding privacy policies is crucial for users as it empowers them to learn about the information that matters to them. Sentences written in a privacy policy document explain privacy practices, and the constituent text spans convey further specific information about that practice. We refer to predicting the privac... | 2021.acl-long.340 | 10.18653/v1/2021.acl-long.340 | null | 2101.00123 | title_snapshot |
2021.acl-long.341 | RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems | https://aclanthology.org/2021.acl-long.341/ | [
"Baolin Peng",
"Chunyuan Li",
"Zhu Zhang",
"Chenguang Zhu",
"Jinchao Li",
"Jianfeng Gao"
] | For task-oriented dialog systems to be maximally useful, it must be able to process conversations in a way that is (1) generalizable with a small number of training examples for new task domains, and (2) robust to user input in various styles, modalities, or domains. In pursuit of these goals, we introduce the RADDLE b... | 2021.acl-long.341 | 10.18653/v1/2021.acl-long.341 | null | 2012.14666 | title_snapshot |
2021.acl-long.342 | Semantic Representation for Dialogue Modeling | https://aclanthology.org/2021.acl-long.342/ | [
"Xuefeng Bai",
"Yulong Chen",
"Linfeng Song",
"Yue Zhang"
] | Although neural models have achieved competitive results in dialogue systems, they have shown limited ability in representing core semantics, such as ignoring important entities. To this end, we exploit Abstract Meaning Representation (AMR) to help dialogue modeling. Compared with the textual input, AMR explicitly prov... | 2021.acl-long.342 | 10.18653/v1/2021.acl-long.342 | null | 2105.10188 | title_snapshot |
2021.acl-long.343 | A Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations | https://aclanthology.org/2021.acl-long.343/ | [
"Chongyang Tao",
"Changyu Chen",
"Jiazhan Feng",
"Ji-Rong Wen",
"Rui Yan"
] | Recently, many studies are emerging towards building a retrieval-based dialogue system that is able to effectively leverage background knowledge (e.g., documents) when conversing with humans. However, it is non-trivial to collect large-scale dialogues that are naturally grounded on the background documents, which hinde... | 2021.acl-long.343 | 10.18653/v1/2021.acl-long.343 | null | null | null |
2021.acl-long.344 | Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks | https://aclanthology.org/2021.acl-long.344/ | [
"Yuanhe Tian",
"Guimin Chen",
"Yan Song",
"Xiang Wan"
] | Syntactic information, especially dependency trees, has been widely used by existing studies to improve relation extraction with better semantic guidance for analyzing the context information associated with the given entities. However, most existing studies suffer from the noise in the dependency trees, especially whe... | 2021.acl-long.344 | 10.18653/v1/2021.acl-long.344 | null | null | null |
2021.acl-long.345 | Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLP | https://aclanthology.org/2021.acl-long.345/ | [
"Anthony Chen",
"Pallavi Gudipati",
"Shayne Longpre",
"Xiao Ling",
"Sameer Singh"
] | Retrieval is a core component for open-domain NLP tasks. In open-domain tasks, multiple entities can share a name, making disambiguation an inherent yet under-explored problem. We propose an evaluation benchmark for assessing the entity disambiguation capabilities of these retrievers, which we call Ambiguous Entity Ret... | 2021.acl-long.345 | 10.18653/v1/2021.acl-long.345 | null | 2106.06830 | title_snapshot |
2021.acl-long.346 | Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards? | https://aclanthology.org/2021.acl-long.346/ | [
"Pedro Rodriguez",
"Joe Barrow",
"Alexander Hoyle",
"John P. Lalor",
"Robin Jia",
"Jordan Boyd-Graber"
] | Leaderboards are widely used in NLP and push the field forward. While leaderboards are a straightforward ranking of NLP models, this simplicity can mask nuances in evaluation items (examples) and subjects (NLP models). Rather than replace leaderboards, we advocate a re-imagining so that they better highlight if and whe... | 2021.acl-long.346 | 10.18653/v1/2021.acl-long.346 | null | null | null |
2021.acl-long.347 | Claim Matching Beyond English to Scale Global Fact-Checking | https://aclanthology.org/2021.acl-long.347/ | [
"Ashkan Kazemi",
"Kiran Garimella",
"Devin Gaffney",
"Scott A. Hale"
] | Manual fact-checking does not scale well to serve the needs of the internet. This issue is further compounded in non-English contexts. In this paper, we discuss claim matching as a possible solution to scale fact-checking. We define claim matching as the task of identifying pairs of textual messages containing claims t... | 2021.acl-long.347 | 10.18653/v1/2021.acl-long.347 | null | 2106.00853 | title_snapshot |
2021.acl-long.348 | SemFace: Pre-training Encoder and Decoder with a Semantic Interface for Neural Machine Translation | https://aclanthology.org/2021.acl-long.348/ | [
"Shuo Ren",
"Long Zhou",
"Shujie Liu",
"Furu Wei",
"Ming Zhou",
"Shuai Ma"
] | While pre-training techniques are working very well in natural language processing, how to pre-train a decoder and effectively use it for neural machine translation (NMT) still remains a tricky issue. The main reason is that the cross-attention module between the encoder and decoder cannot be pre-trained, and the combi... | 2021.acl-long.348 | 10.18653/v1/2021.acl-long.348 | null | null | null |
2021.acl-long.349 | Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models | https://aclanthology.org/2021.acl-long.349/ | [
"Sumanta Bhattacharyya",
"Amirmohammad Rooshenas",
"Subhajit Naskar",
"Simeng Sun",
"Mohit Iyyer",
"Andrew McCallum"
] | The discrepancy between maximum likelihood estimation (MLE) and task measures such as BLEU score has been studied before for autoregressive neural machine translation (NMT) and resulted in alternative training algorithms (Ranzato et al., 2016; Norouzi et al., 2016; Shen et al., 2016; Wu et al., 2018). However, MLE trai... | 2021.acl-long.349 | 10.18653/v1/2021.acl-long.349 | null | 2009.13267 | title_snapshot |
2021.acl-long.350 | Syntax-augmented Multilingual BERT for Cross-lingual Transfer | https://aclanthology.org/2021.acl-long.350/ | [
"Wasi Ahmad",
"Haoran Li",
"Kai-Wei Chang",
"Yashar Mehdad"
] | In recent years, we have seen a colossal effort in pre-training multilingual text encoders using large-scale corpora in many languages to facilitate cross-lingual transfer learning. However, due to typological differences across languages, the cross-lingual transfer is challenging. Nevertheless, language syntax, e.g., ... | 2021.acl-long.350 | 10.18653/v1/2021.acl-long.350 | null | 2106.02134 | title_snapshot |
2021.acl-long.351 | How to Adapt Your Pretrained Multilingual Model to 1600 Languages | https://aclanthology.org/2021.acl-long.351/ | [
"Abteen Ebrahimi",
"Katharina Kann"
] | Pretrained multilingual models (PMMs) enable zero-shot learning via cross-lingual transfer, performing best for languages seen during pretraining. While methods exist to improve performance for unseen languages, they have almost exclusively been evaluated using amounts of raw text only available for a small fraction of... | 2021.acl-long.351 | 10.18653/v1/2021.acl-long.351 | null | 2106.02124 | title_snapshot |
2021.acl-long.352 | Weakly Supervised Named Entity Tagging with Learnable Logical Rules | https://aclanthology.org/2021.acl-long.352/ | [
"Jiacheng Li",
"Haibo Ding",
"Jingbo Shang",
"Julian McAuley",
"Zhe Feng"
] | We study the problem of building entity tagging systems by using a few rules as weak supervision. Previous methods mostly focus on disambiguating entity types based on contexts and expert-provided rules, while assuming entity spans are given. In this work, we propose a novel method TALLOR that bootstraps high-quality l... | 2021.acl-long.352 | 10.18653/v1/2021.acl-long.352 | null | 2107.02282 | title_snapshot |
2021.acl-long.353 | Prefix-Tuning: Optimizing Continuous Prompts for Generation | https://aclanthology.org/2021.acl-long.353/ | [
"Xiang Lisa Li",
"Percy Liang"
] | Fine-tuning is the de facto way of leveraging large pretrained language models for downstream tasks. However, fine-tuning modifies all the language model parameters and therefore necessitates storing a full copy for each task. In this paper, we propose prefix-tuning, a lightweight alternative to fine-tuning for natural... | 2021.acl-long.353 | 10.18653/v1/2021.acl-long.353 | null | 2101.00190 | title_snapshot |
2021.acl-long.354 | One2Set: Generating Diverse Keyphrases as a Set | https://aclanthology.org/2021.acl-long.354/ | [
"Jiacheng Ye",
"Tao Gui",
"Yichao Luo",
"Yige Xu",
"Qi Zhang"
] | Recently, the sequence-to-sequence models have made remarkable progress on the task of keyphrase generation (KG) by concatenating multiple keyphrases in a predefined order as a target sequence during training. However, the keyphrases are inherently an unordered set rather than an ordered sequence. Imposing a predefined... | 2021.acl-long.354 | 10.18653/v1/2021.acl-long.354 | null | 2105.11134 | title_snapshot |
2021.acl-long.355 | Continuous Language Generative Flow | https://aclanthology.org/2021.acl-long.355/ | [
"Zineng Tang",
"Shiyue Zhang",
"Hyounghun Kim",
"Mohit Bansal"
] | Recent years have witnessed various types of generative models for natural language generation (NLG), especially RNNs or transformer based sequence-to-sequence models, as well as variational autoencoder (VAE) and generative adversarial network (GAN) based models. However, flow-based generative models, which achieve str... | 2021.acl-long.355 | 10.18653/v1/2021.acl-long.355 | null | null | null |
2021.acl-long.356 | TWAG: A Topic-Guided Wikipedia Abstract Generator | https://aclanthology.org/2021.acl-long.356/ | [
"Fangwei Zhu",
"Shangqing Tu",
"Jiaxin Shi",
"Juanzi Li",
"Lei Hou",
"Tong Cui"
] | Wikipedia abstract generation aims to distill a Wikipedia abstract from web sources and has met significant success by adopting multi-document summarization techniques. However, previous works generally view the abstract as plain text, ignoring the fact that it is a description of a certain entity and can be decomposed... | 2021.acl-long.356 | 10.18653/v1/2021.acl-long.356 | null | 2106.15135 | title_snapshot |
2021.acl-long.357 | ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data | https://aclanthology.org/2021.acl-long.357/ | [
"Woojeong Jin",
"Rahul Khanna",
"Suji Kim",
"Dong-Ho Lee",
"Fred Morstatter",
"Aram Galstyan",
"Xiang Ren"
] | Event forecasting is a challenging, yet important task, as humans seek to constantly plan for the future. Existing automated forecasting studies rely mostly on structured data, such as time-series or event-based knowledge graphs, to help predict future events. In this work, we aim to formulate a task, construct a datas... | 2021.acl-long.357 | 10.18653/v1/2021.acl-long.357 | null | 2005.00792 | title_snapshot |
2021.acl-long.358 | Recursive Tree-Structured Self-Attention for Answer Sentence Selection | https://aclanthology.org/2021.acl-long.358/ | [
"Khalil Mrini",
"Emilia Farcas",
"Ndapa Nakashole"
] | Syntactic structure is an important component of natural language text. Recent top-performing models in Answer Sentence Selection (AS2) use self-attention and transfer learning, but not syntactic structure. Tree structures have shown strong performance in tasks with sentence pair input like semantic relatedness. We inv... | 2021.acl-long.358 | 10.18653/v1/2021.acl-long.358 | null | null | null |
2021.acl-long.359 | How Knowledge Graph and Attention Help? A Qualitative Analysis into Bag-level Relation Extraction | https://aclanthology.org/2021.acl-long.359/ | [
"Zikun Hu",
"Yixin Cao",
"Lifu Huang",
"Tat-Seng Chua"
] | Knowledge Graph (KG) and attention mechanism have been demonstrated effective in introducing and selecting useful information for weakly supervised methods. However, only qualitative analysis and ablation study are provided as evidence. In this paper, we contribute a dataset and propose a paradigm to quantitatively eva... | 2021.acl-long.359 | 10.18653/v1/2021.acl-long.359 | null | 2107.12064 | title_judge |
2021.acl-long.360 | Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction | https://aclanthology.org/2021.acl-long.360/ | [
"Kaiwen Wei",
"Xian Sun",
"Zequn Zhang",
"Jingyuan Zhang",
"Guo Zhi",
"Li Jin"
] | Implicit Event Argument Extraction seeks to identify arguments that play direct or implicit roles in a given event. However, most prior works focus on capturing direct relations between arguments and the event trigger. The lack of reasoning ability brings many challenges to the extraction of implicit arguments. In this... | 2021.acl-long.360 | 10.18653/v1/2021.acl-long.360 | null | null | null |
2021.acl-long.361 | Element Intervention for Open Relation Extraction | https://aclanthology.org/2021.acl-long.361/ | [
"Fangchao Liu",
"Lingyong Yan",
"Hongyu Lin",
"Xianpei Han",
"Le Sun"
] | Open relation extraction aims to cluster relation instances referring to the same underlying relation, which is a critical step for general relation extraction. Current OpenRE models are commonly trained on the datasets generated from distant supervision, which often results in instability and makes the model easily co... | 2021.acl-long.361 | 10.18653/v1/2021.acl-long.361 | null | 2106.09558 | title_snapshot |
2021.acl-long.362 | AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding | https://aclanthology.org/2021.acl-long.362/ | [
"Jun Yan",
"Nasser Zalmout",
"Yan Liang",
"Christan Grant",
"Xiang Ren",
"Xin Luna Dong"
] | Automatic extraction of product attribute values is an important enabling technology in e-Commerce platforms. This task is usually modeled using sequence labeling architectures, with several extensions to handle multi-attribute extraction. One line of previous work constructs attribute-specific models, through separate... | 2021.acl-long.362 | 10.18653/v1/2021.acl-long.362 | null | 2106.02318 | title_snapshot |
2021.acl-long.363 | CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction | https://aclanthology.org/2021.acl-long.363/ | [
"Zhengbao Jiang",
"Jialong Han",
"Bunyamin Sisman",
"Xin Luna Dong"
] | Integrating extracted knowledge from the Web to knowledge graphs (KGs) can facilitate tasks like question answering. We study relation integration that aims to align free-text relations in subject-relation-object extractions to relations in a target KG. To address the challenge that free-text relations are ambiguous, p... | 2021.acl-long.363 | 10.18653/v1/2021.acl-long.363 | null | 2106.00793 | title_snapshot |
2021.acl-long.364 | Benchmarking Scalable Methods for Streaming Cross Document Entity Coreference | https://aclanthology.org/2021.acl-long.364/ | [
"Robert L Logan IV",
"Andrew McCallum",
"Sameer Singh",
"Dan Bikel"
] | Streaming cross document entity coreference (CDC) systems disambiguate mentions of named entities in a scalable manner via incremental clustering. Unlike other approaches for named entity disambiguation (e.g., entity linking), streaming CDC allows for the disambiguation of entities that are unknown at inference time. T... | 2021.acl-long.364 | 10.18653/v1/2021.acl-long.364 | null | null | null |
2021.acl-long.365 | Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs | https://aclanthology.org/2021.acl-long.365/ | [
"Zixuan Li",
"Xiaolong Jin",
"Saiping Guan",
"Wei Li",
"Jiafeng Guo",
"Yuanzhuo Wang",
"Xueqi Cheng"
] | Temporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great challenges to existing models. When facing a prediction task, human beings usually search useful historical information (i.e., clues) in their memori... | 2021.acl-long.365 | 10.18653/v1/2021.acl-long.365 | null | 2106.00327 | title_snapshot |
2021.acl-long.366 | Employing Argumentation Knowledge Graphs for Neural Argument Generation | https://aclanthology.org/2021.acl-long.366/ | [
"Khalid Al Khatib",
"Lukas Trautner",
"Henning Wachsmuth",
"Yufang Hou",
"Benno Stein"
] | Generating high-quality arguments, while being challenging, may benefit a wide range of downstream applications, such as writing assistants and argument search engines. Motivated by the effectiveness of utilizing knowledge graphs for supporting general text generation tasks, this paper investigates the usage of argumen... | 2021.acl-long.366 | 10.18653/v1/2021.acl-long.366 | null | null | null |
2021.acl-long.367 | Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction | https://aclanthology.org/2021.acl-long.367/ | [
"Lu Xu",
"Yew Ken Chia",
"Lidong Bing"
] | Aspect Sentiment Triplet Extraction (ASTE) is the most recent subtask of ABSA which outputs triplets of an aspect target, its associated sentiment, and the corresponding opinion term. Recent models perform the triplet extraction in an end-to-end manner but heavily rely on the interactions between each target word and o... | 2021.acl-long.367 | 10.18653/v1/2021.acl-long.367 | null | 2107.12214 | title_snapshot |
2021.acl-long.368 | On Compositional Generalization of Neural Machine Translation | https://aclanthology.org/2021.acl-long.368/ | [
"Yafu Li",
"Yongjing Yin",
"Yulong Chen",
"Yue Zhang"
] | Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks such as WMT. However, there still exist significant issues such as robustness, domain generalization, etc. In this paper, we study NMT models from the perspective of compositional generalization by building a ben... | 2021.acl-long.368 | 10.18653/v1/2021.acl-long.368 | null | 2105.14802 | title_snapshot |
2021.acl-long.369 | Mask-Align: Self-Supervised Neural Word Alignment | https://aclanthology.org/2021.acl-long.369/ | [
"Chi Chen",
"Maosong Sun",
"Yang Liu"
] | Word alignment, which aims to align translationally equivalent words between source and target sentences, plays an important role in many natural language processing tasks. Current unsupervised neural alignment methods focus on inducing alignments from neural machine translation models, which does not leverage the full... | 2021.acl-long.369 | 10.18653/v1/2021.acl-long.369 | null | 2012.07162 | title_snapshot |
2021.acl-long.370 | GWLAN: General Word-Level AutocompletioN for Computer-Aided Translation | https://aclanthology.org/2021.acl-long.370/ | [
"Huayang Li",
"Lemao Liu",
"Guoping Huang",
"Shuming Shi"
] | Computer-aided translation (CAT), the use of software to assist a human translator in the translation process, has been proven to be useful in enhancing the productivity of human translators. Autocompletion, which suggests translation results according to the text pieces provided by human translators, is a core functio... | 2021.acl-long.370 | 10.18653/v1/2021.acl-long.370 | null | 2105.14913 | title_snapshot |
2021.acl-long.371 | De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention | https://aclanthology.org/2021.acl-long.371/ | [
"Wenkai Zhang",
"Hongyu Lin",
"Xianpei Han",
"Le Sun"
] | Distant supervision tackles the data bottleneck in NER by automatically generating training instances via dictionary matching. Unfortunately, the learning of DS-NER is severely dictionary-biased, which suffers from spurious correlations and therefore undermines the effectiveness and the robustness of the learned models... | 2021.acl-long.371 | 10.18653/v1/2021.acl-long.371 | null | 2106.09233 | title_snapshot |
2021.acl-long.372 | A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition | https://aclanthology.org/2021.acl-long.372/ | [
"Fei Li",
"ZhiChao Lin",
"Meishan Zhang",
"Donghong Ji"
] | Research on overlapped and discontinuous named entity recognition (NER) has received increasing attention. The majority of previous work focuses on either overlapped or discontinuous entities. In this paper, we propose a novel span-based model that can recognize both overlapped and discontinuous entities jointly. The m... | 2021.acl-long.372 | 10.18653/v1/2021.acl-long.372 | null | 2106.14373 | title_snapshot |
2021.acl-long.373 | MLBiNet: A Cross-Sentence Collective Event Detection Network | https://aclanthology.org/2021.acl-long.373/ | [
"Dongfang Lou",
"Zhilin Liao",
"Shumin Deng",
"Ningyu Zhang",
"Huajun Chen"
] | We consider the problem of collectively detecting multiple events, particularly in cross-sentence settings. The key to dealing with the problem is to encode semantic information and model event inter-dependency at a document-level. In this paper, we reformulate it as a Seq2Seq task and propose a Multi-Layer Bidirection... | 2021.acl-long.373 | 10.18653/v1/2021.acl-long.373 | null | 2105.09458 | title_snapshot |
2021.acl-long.374 | Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution | https://aclanthology.org/2021.acl-long.374/ | [
"Hieu Minh Tran",
"Duy Phung",
"Thien Huu Nguyen"
] | We study the problem of event coreference resolution (ECR) that seeks to group coreferent event mentions into the same clusters. Deep learning methods have recently been applied for this task to deliver state-of-the-art performance. However, existing deep learning models for ECR are limited in that they cannot exploit ... | 2021.acl-long.374 | 10.18653/v1/2021.acl-long.374 | null | null | null |
2021.acl-long.375 | StereoRel: Relational Triple Extraction from a Stereoscopic Perspective | https://aclanthology.org/2021.acl-long.375/ | [
"Xuetao Tian",
"Liping Jing",
"Lu He",
"Feng Liu"
] | Relational triple extraction is critical to understanding massive text corpora and constructing large-scale knowledge graph, which has attracted increasing research interest. However, existing studies still face some challenging issues, including information loss, error propagation and ignoring the interaction between ... | 2021.acl-long.375 | 10.18653/v1/2021.acl-long.375 | null | null | null |
2021.acl-long.376 | Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks | https://aclanthology.org/2021.acl-long.376/ | [
"Pengfei Cao",
"Xinyu Zuo",
"Yubo Chen",
"Kang Liu",
"Jun Zhao",
"Yuguang Chen",
"Weihua Peng"
] | Identifying causal relations of events is an important task in natural language processing area. However, the task is very challenging, because event causality is usually expressed in diverse forms that often lack explicit causal clues. Existing methods cannot handle well the problem, especially in the condition of lac... | 2021.acl-long.376 | 10.18653/v1/2021.acl-long.376 | null | null | null |
2021.acl-long.377 | Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution | https://aclanthology.org/2021.acl-long.377/ | [
"Fanchao Qi",
"Yuan Yao",
"Sophia Xu",
"Zhiyuan Liu",
"Maosong Sun"
] | Recent studies show that neural natural language processing (NLP) models are vulnerable to backdoor attacks. Injected with backdoors, models perform normally on benign examples but produce attacker-specified predictions when the backdoor is activated, presenting serious security threats to real-world applications. Sinc... | 2021.acl-long.377 | 10.18653/v1/2021.acl-long.377 | null | 2106.06361 | title_snapshot |
2021.acl-long.378 | Parameter-Efficient Transfer Learning with Diff Pruning | https://aclanthology.org/2021.acl-long.378/ | [
"Demi Guo",
"Alexander Rush",
"Yoon Kim"
] | The large size of pretrained networks makes them difficult to deploy for multiple tasks in storage-constrained settings. Diff pruning enables parameter-efficient transfer learning that scales well with new tasks. The approach learns a task-specific “diff” vector that extends the original pretrained parameters. This dif... | 2021.acl-long.378 | 10.18653/v1/2021.acl-long.378 | null | 2012.07463 | title_snapshot |
2021.acl-long.379 | R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modeling | https://aclanthology.org/2021.acl-long.379/ | [
"Xiang Hu",
"Haitao Mi",
"Zujie Wen",
"Yafang Wang",
"Yi Su",
"Jing Zheng",
"Gerard de Melo"
] | Human language understanding operates at multiple levels of granularity (e.g., words, phrases, and sentences) with increasing levels of abstraction that can be hierarchically combined. However, existing deep models with stacked layers do not explicitly model any sort of hierarchical process. In this paper, we propose a... | 2021.acl-long.379 | 10.18653/v1/2021.acl-long.379 | null | 2107.00967 | title_snapshot |
2021.acl-long.380 | Risk Minimization for Zero-shot Sequence Labeling | https://aclanthology.org/2021.acl-long.380/ | [
"Zechuan Hu",
"Yong Jiang",
"Nguyen Bach",
"Tao Wang",
"Zhongqiang Huang",
"Fei Huang",
"Kewei Tu"
] | Zero-shot sequence labeling aims to build a sequence labeler without human-annotated datasets. One straightforward approach is utilizing existing systems (source models) to generate pseudo-labeled datasets and train a target sequence labeler accordingly. However, due to the gap between the source and the target languag... | 2021.acl-long.380 | 10.18653/v1/2021.acl-long.380 | null | null | null |
2021.acl-long.381 | WARP: Word-level Adversarial ReProgramming | https://aclanthology.org/2021.acl-long.381/ | [
"Karen Hambardzumyan",
"Hrant Khachatrian",
"Jonathan May"
] | Transfer learning from pretrained language models recently became the dominant approach for solving many NLP tasks. A common approach to transfer learning for multiple tasks that maximize parameter sharing trains one or more task-specific layers on top of the language model. In this paper, we present an alternative app... | 2021.acl-long.381 | 10.18653/v1/2021.acl-long.381 | null | 2101.00121 | title_snapshot |
2021.acl-long.382 | Lexicon Learning for Few Shot Sequence Modeling | https://aclanthology.org/2021.acl-long.382/ | [
"Ekin Akyurek",
"Jacob Andreas"
] | Sequence-to-sequence transduction is the core problem in language processing applications as diverse as semantic parsing, machine translation, and instruction following. The neural network models that provide the dominant solution to these problems are brittle, especially in low-resource settings: they fail to generali... | 2021.acl-long.382 | 10.18653/v1/2021.acl-long.382 | null | 2106.03993 | title_judge |
2021.acl-long.383 | Personalized Transformer for Explainable Recommendation | https://aclanthology.org/2021.acl-long.383/ | [
"Lei Li",
"Yongfeng Zhang",
"Li Chen"
] | Personalization of natural language generation plays a vital role in a large spectrum of tasks, such as explainable recommendation, review summarization and dialog systems. In these tasks, user and item IDs are important identifiers for personalization. Transformer, which is demonstrated with strong language modeling c... | 2021.acl-long.383 | 10.18653/v1/2021.acl-long.383 | null | 2105.11601 | title_snapshot |
2021.acl-long.384 | Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques | https://aclanthology.org/2021.acl-long.384/ | [
"Kundan Krishna",
"Sopan Khosla",
"Jeffrey Bigham",
"Zachary C. Lipton"
] | Following each patient visit, physicians draft long semi-structured clinical summaries called SOAP notes. While invaluable to clinicians and researchers, creating digital SOAP notes is burdensome, contributing to physician burnout. In this paper, we introduce the first complete pipelines to leverage deep summarization ... | 2021.acl-long.384 | 10.18653/v1/2021.acl-long.384 | null | 2005.01795 | title_snapshot |
2021.acl-long.385 | Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction | https://aclanthology.org/2021.acl-long.385/ | [
"Piji Li",
"Shuming Shi"
] | We investigate the problem of Chinese Grammatical Error Correction (CGEC) and present a new framework named Tail-to-Tail (TtT) non-autoregressive sequence prediction to address the deep issues hidden in CGEC. Considering that most tokens are correct and can be conveyed directly from source to target, and the error posi... | 2021.acl-long.385 | 10.18653/v1/2021.acl-long.385 | null | 2106.01609 | title_snapshot |
2021.acl-long.386 | Early Detection of Sexual Predators in Chats | https://aclanthology.org/2021.acl-long.386/ | [
"Matthias Vogt",
"Ulf Leser",
"Alan Akbik"
] | An important risk that children face today is online grooming, where a so-called sexual predator establishes an emotional connection with a minor online with the objective of sexual abuse. Prior work has sought to automatically identify grooming chats, but only after an incidence has already happened in the context of ... | 2021.acl-long.386 | 10.18653/v1/2021.acl-long.386 | null | null | null |
2021.acl-long.387 | Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation | https://aclanthology.org/2021.acl-long.387/ | [
"Xingyi Yang",
"Muchao Ye",
"Quanzeng You",
"Fenglong Ma"
] | Medical report generation is one of the most challenging tasks in medical image analysis. Although existing approaches have achieved promising results, they either require a predefined template database in order to retrieve sentences or ignore the hierarchical nature of medical report generation. To address these issue... | 2021.acl-long.387 | 10.18653/v1/2021.acl-long.387 | null | 2106.06471 | title_snapshot |
2021.acl-long.388 | Concept-Based Label Embedding via Dynamic Routing for Hierarchical Text Classification | https://aclanthology.org/2021.acl-long.388/ | [
"Xuepeng Wang",
"Li Zhao",
"Bing Liu",
"Tao Chen",
"Feng Zhang",
"Di Wang"
] | Hierarchical Text Classification (HTC) is a challenging task that categorizes a textual description within a taxonomic hierarchy. Most of the existing methods focus on modeling the text. Recently, researchers attempt to model the class representations with some resources (e.g., external dictionaries). However, the conc... | 2021.acl-long.388 | 10.18653/v1/2021.acl-long.388 | null | null | null |
2021.acl-long.389 | VisualSparta: An Embarrassingly Simple Approach to Large-scale Text-to-Image Search with Weighted Bag-of-words | https://aclanthology.org/2021.acl-long.389/ | [
"Xiaopeng Lu",
"Tiancheng Zhao",
"Kyusong Lee"
] | Text-to-image retrieval is an essential task in cross-modal information retrieval, i.e., retrieving relevant images from a large and unlabelled dataset given textual queries. In this paper, we propose VisualSparta, a novel (Visual-text Sparse Transformer Matching) model that shows significant improvement in terms of bo... | 2021.acl-long.389 | 10.18653/v1/2021.acl-long.389 | null | 2101.00265 | title_snapshot |
2021.acl-long.390 | Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision | https://aclanthology.org/2021.acl-long.390/ | [
"Si Sun",
"Yingzhuo Qian",
"Zhenghao Liu",
"Chenyan Xiong",
"Kaitao Zhang",
"Jie Bao",
"Zhiyuan Liu",
"Paul Bennett"
] | The effectiveness of Neural Information Retrieval (Neu-IR) often depends on a large scale of in-domain relevance training signals, which are not always available in real-world ranking scenarios. To democratize the benefits of Neu-IR, this paper presents MetaAdaptRank, a domain adaptive learning method that generalizes ... | 2021.acl-long.390 | 10.18653/v1/2021.acl-long.390 | null | 2012.14862 | title_snapshot |
2021.acl-long.391 | Semi-Supervised Text Classification with Balanced Deep Representation Distributions | https://aclanthology.org/2021.acl-long.391/ | [
"Changchun Li",
"Ximing Li",
"Jihong Ouyang"
] | Semi-Supervised Text Classification (SSTC) mainly works under the spirit of self-training. They initialize the deep classifier by training over labeled texts; and then alternatively predict unlabeled texts as their pseudo-labels and train the deep classifier over the mixture of labeled and pseudo-labeled texts. Natural... | 2021.acl-long.391 | 10.18653/v1/2021.acl-long.391 | null | null | null |
2021.acl-long.392 | Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval | https://aclanthology.org/2021.acl-long.392/ | [
"Hongyin Tang",
"Xingwu Sun",
"Beihong Jin",
"Jingang Wang",
"Fuzheng Zhang",
"Wei Wu"
] | Recently, the retrieval models based on dense representations have been gradually applied in the first stage of the document retrieval tasks, showing better performance than traditional sparse vector space models. To obtain high efficiency, the basic structure of these models is Bi-encoder in most cases. However, this ... | 2021.acl-long.392 | 10.18653/v1/2021.acl-long.392 | null | 2105.03599 | title_snapshot |
2021.acl-long.393 | ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer | https://aclanthology.org/2021.acl-long.393/ | [
"Yuanmeng Yan",
"Rumei Li",
"Sirui Wang",
"Fuzheng Zhang",
"Wei Wu",
"Weiran Xu"
] | Learning high-quality sentence representations benefits a wide range of natural language processing tasks. Though BERT-based pre-trained language models achieve high performance on many downstream tasks, the native derived sentence representations are proved to be collapsed and thus produce a poor performance on the se... | 2021.acl-long.393 | 10.18653/v1/2021.acl-long.393 | null | 2105.11741 | title_snapshot |
2021.acl-long.394 | Exploring Dynamic Selection of Branch Expansion Orders for Code Generation | https://aclanthology.org/2021.acl-long.394/ | [
"Hui Jiang",
"Chulun Zhou",
"Fandong Meng",
"Biao Zhang",
"Jie Zhou",
"Degen Huang",
"Qingqiang Wu",
"Jinsong Su"
] | Due to the great potential in facilitating software development, code generation has attracted increasing attention recently. Generally, dominant models are Seq2Tree models, which convert the input natural language description into a sequence of tree-construction actions corresponding to the pre-order traversal of an A... | 2021.acl-long.394 | 10.18653/v1/2021.acl-long.394 | null | 2106.00261 | title_snapshot |
2021.acl-long.395 | COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion | https://aclanthology.org/2021.acl-long.395/ | [
"Debjit Paul",
"Anette Frank"
] | Despite recent successes of large pre-trained language models in solving reasoning tasks, their inference capabilities remain opaque. We posit that such models can be made more interpretable by explicitly generating interim inference rules, and using them to guide the generation of task-specific textual outputs. In thi... | 2021.acl-long.395 | 10.18653/v1/2021.acl-long.395 | null | 2106.02497 | title_snapshot |
2021.acl-long.396 | Reasoning over Entity-Action-Location Graph for Procedural Text Understanding | https://aclanthology.org/2021.acl-long.396/ | [
"Hao Huang",
"Xiubo Geng",
"Jian Pei",
"Guodong Long",
"Daxin Jiang"
] | Procedural text understanding aims at tracking the states (e.g., create, move, destroy) and locations of the entities mentioned in a given paragraph. To effectively track the states and locations, it is essential to capture the rich semantic relations between entities, actions, and locations in the paragraph. Although ... | 2021.acl-long.396 | 10.18653/v1/2021.acl-long.396 | null | null | null |
2021.acl-long.397 | From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding | https://aclanthology.org/2021.acl-long.397/ | [
"Shan Wu",
"Bo Chen",
"Chunlei Xin",
"Xianpei Han",
"Le Sun",
"Weipeng Zhang",
"Jiansong Chen",
"Fan Yang",
"Xunliang Cai"
] | Semantic parsing is challenging due to the structure gap and the semantic gap between utterances and logical forms. In this paper, we propose an unsupervised semantic parsing method - Synchronous Semantic Decoding (SSD), which can simultaneously resolve the semantic gap and the structure gap by jointly leveraging parap... | 2021.acl-long.397 | 10.18653/v1/2021.acl-long.397 | null | 2106.06228 | title_snapshot |
2021.acl-long.398 | Pre-training Universal Language Representation | https://aclanthology.org/2021.acl-long.398/ | [
"Yian Li",
"Hai Zhao"
] | Despite the well-developed cut-edge representation learning for language, most language representation models usually focus on specific levels of linguistic units. This work introduces universal language representation learning, i.e., embeddings of different levels of linguistic units or text with quite diverse lengths... | 2021.acl-long.398 | 10.18653/v1/2021.acl-long.398 | null | 2105.14478 | title_snapshot |
2021.acl-long.399 | Structural Pre-training for Dialogue Comprehension | https://aclanthology.org/2021.acl-long.399/ | [
"Zhuosheng Zhang",
"Hai Zhao"
] | Pre-trained language models (PrLMs) have demonstrated superior performance due to their strong ability to learn universal language representations from self-supervised pre-training. However, even with the help of the powerful PrLMs, it is still challenging to effectively capture task-related knowledge from dialogue tex... | 2021.acl-long.399 | 10.18653/v1/2021.acl-long.399 | null | 2105.10956 | title_snapshot |
2021.acl-long.400 | AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models | https://aclanthology.org/2021.acl-long.400/ | [
"Yichun Yin",
"Cheng Chen",
"Lifeng Shang",
"Xin Jiang",
"Xiao Chen",
"Qun Liu"
] | Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. Few studies have been conducted to explore... | 2021.acl-long.400 | 10.18653/v1/2021.acl-long.400 | null | 2107.13686 | title_snapshot |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.