EMNLP
Collection
Accepted papers for EMNLP (Conference on Empirical Methods in Natural Language Processing), one dataset per year. • 13 items • Updated
paper_id stringlengths 17 19 | title stringlengths 27 144 | paper_url stringlengths 43 45 | authors listlengths 1 37 | abstract large_stringlengths 396 1.69k | anthology_id stringlengths 17 19 | doi stringlengths 29 31 | award stringclasses 0
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2021.emnlp-main.1 | AligNART: Non-autoregressive Neural Machine Translation by Jointly Learning to Estimate Alignment and Translate | https://aclanthology.org/2021.emnlp-main.1/ | [
"Jongyoon Song",
"Sungwon Kim",
"Sungroh Yoon"
] | Non-autoregressive neural machine translation (NART) models suffer from the multi-modality problem which causes translation inconsistency such as token repetition. Most recent approaches have attempted to solve this problem by implicitly modeling dependencies between outputs. In this paper, we introduce AligNART, which... | 2021.emnlp-main.1 | 10.18653/v1/2021.emnlp-main.1 | null | 2109.06481 | title_snapshot |
2021.emnlp-main.2 | Zero-Shot Cross-Lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders | https://aclanthology.org/2021.emnlp-main.2/ | [
"Guanhua Chen",
"Shuming Ma",
"Yun Chen",
"Li Dong",
"Dongdong Zhang",
"Jia Pan",
"Wenping Wang",
"Furu Wei"
] | Previous work mainly focuses on improving cross-lingual transfer for NLU tasks with a multilingual pretrained encoder (MPE), or improving the performance on supervised machine translation with BERT. However, it is under-explored that whether the MPE can help to facilitate the cross-lingual transferability of NMT model.... | 2021.emnlp-main.2 | 10.18653/v1/2021.emnlp-main.2 | null | 2104.08757 | title_snapshot |
2021.emnlp-main.3 | ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora | https://aclanthology.org/2021.emnlp-main.3/ | [
"Xuan Ouyang",
"Shuohuan Wang",
"Chao Pang",
"Yu Sun",
"Hao Tian",
"Hua Wu",
"Haifeng Wang"
] | Recent studies have demonstrated that pre-trained cross-lingual models achieve impressive performance in downstream cross-lingual tasks. This improvement benefits from learning a large amount of monolingual and parallel corpora. Although it is generally acknowledged that parallel corpora are critical for improving the ... | 2021.emnlp-main.3 | 10.18653/v1/2021.emnlp-main.3 | null | 2012.15674 | title_snapshot |
2021.emnlp-main.4 | Cross Attention Augmented Transducer Networks for Simultaneous Translation | https://aclanthology.org/2021.emnlp-main.4/ | [
"Dan Liu",
"Mengge Du",
"Xiaoxi Li",
"Ya Li",
"Enhong Chen"
] | This paper proposes a novel architecture, Cross Attention Augmented Transducer (CAAT), for simultaneous translation. The framework aims to jointly optimize the policy and translation models. To effectively consider all possible READ-WRITE simultaneous translation action paths, we adapt the online automatic speech recog... | 2021.emnlp-main.4 | 10.18653/v1/2021.emnlp-main.4 | null | null | null |
2021.emnlp-main.5 | Translating Headers of Tabular Data: A Pilot Study of Schema Translation | https://aclanthology.org/2021.emnlp-main.5/ | [
"Kunrui Zhu",
"Yan Gao",
"Jiaqi Guo",
"Jian-Guang Lou"
] | Schema translation is the task of automatically translating headers of tabular data from one language to another. High-quality schema translation plays an important role in cross-lingual table searching, understanding and analysis. Despite its importance, schema translation is not well studied in the community, and sta... | 2021.emnlp-main.5 | 10.18653/v1/2021.emnlp-main.5 | null | null | null |
2021.emnlp-main.6 | Towards Making the Most of Dialogue Characteristics for Neural Chat Translation | https://aclanthology.org/2021.emnlp-main.6/ | [
"Yunlong Liang",
"Chulun Zhou",
"Fandong Meng",
"Jinan Xu",
"Yufeng Chen",
"Jinsong Su",
"Jie Zhou"
] | Neural Chat Translation (NCT) aims to translate conversational text between speakers of different languages. Despite the promising performance of sentence-level and context-aware neural machine translation models, there still remain limitations in current NCT models because the inherent dialogue characteristics of chat... | 2021.emnlp-main.6 | 10.18653/v1/2021.emnlp-main.6 | null | 2109.00668 | title_snapshot |
2021.emnlp-main.7 | Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining | https://aclanthology.org/2021.emnlp-main.7/ | [
"Yicheng Zou",
"Bolin Zhu",
"Xingwu Hu",
"Tao Gui",
"Qi Zhang"
] | With the rapid increase in the volume of dialogue data from daily life, there is a growing demand for dialogue summarization. Unfortunately, training a large summarization model is generally infeasible due to the inadequacy of dialogue data with annotated summaries. Most existing works for low-resource dialogue summari... | 2021.emnlp-main.7 | 10.18653/v1/2021.emnlp-main.7 | null | 2109.04080 | title_snapshot |
2021.emnlp-main.8 | Controllable Neural Dialogue Summarization with Personal Named Entity Planning | https://aclanthology.org/2021.emnlp-main.8/ | [
"Zhengyuan Liu",
"Nancy Chen"
] | In this paper, we propose a controllable neural generation framework that can flexibly guide dialogue summarization with personal named entity planning. The conditional sequences are modulated to decide what types of information or what perspective to focus on when forming summaries to tackle the under-constrained prob... | 2021.emnlp-main.8 | 10.18653/v1/2021.emnlp-main.8 | null | 2109.13070 | title_snapshot |
2021.emnlp-main.9 | Fine-grained Factual Consistency Assessment for Abstractive Summarization Models | https://aclanthology.org/2021.emnlp-main.9/ | [
"Sen Zhang",
"Jianwei Niu",
"Chuyuan Wei"
] | Factual inconsistencies existed in the output of abstractive summarization models with original documents are frequently presented. Fact consistency assessment requires the reasoning capability to find subtle clues to identify whether a model-generated summary is consistent with the original document. This paper propos... | 2021.emnlp-main.9 | 10.18653/v1/2021.emnlp-main.9 | null | null | null |
2021.emnlp-main.10 | Decision-Focused Summarization | https://aclanthology.org/2021.emnlp-main.10/ | [
"Chao-Chun Hsu",
"Chenhao Tan"
] | Relevance in summarization is typically de- fined based on textual information alone, without incorporating insights about a particular decision. As a result, to support risk analysis of pancreatic cancer, summaries of medical notes may include irrelevant information such as a knee injury. We propose a novel problem, d... | 2021.emnlp-main.10 | 10.18653/v1/2021.emnlp-main.10 | null | 2109.06896 | title_snapshot |
2021.emnlp-main.11 | Multiplex Graph Neural Network for Extractive Text Summarization | https://aclanthology.org/2021.emnlp-main.11/ | [
"Baoyu Jing",
"Zeyu You",
"Tao Yang",
"Wei Fan",
"Hanghang Tong"
] | Extractive text summarization aims at extracting the most representative sentences from a given document as its summary. To extract a good summary from a long text document, sentence embedding plays an important role. Recent studies have leveraged graph neural networks to capture the inter-sentential relationship (e.g.... | 2021.emnlp-main.11 | 10.18653/v1/2021.emnlp-main.11 | null | 2108.12870 | title_snapshot |
2021.emnlp-main.12 | A Thorough Evaluation of Task-Specific Pretraining for Summarization | https://aclanthology.org/2021.emnlp-main.12/ | [
"Sascha Rothe",
"Joshua Maynez",
"Shashi Narayan"
] | Task-agnostic pretraining objectives like masked language models or corrupted span prediction are applicable to a wide range of NLP downstream tasks (Raffel et al.,2019), but are outperformed by task-specific pretraining objectives like predicting extracted gap sentences on summarization (Zhang et al.,2020). We compare... | 2021.emnlp-main.12 | 10.18653/v1/2021.emnlp-main.12 | null | null | null |
2021.emnlp-main.13 | HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization | https://aclanthology.org/2021.emnlp-main.13/ | [
"Ye Liu",
"Jianguo Zhang",
"Yao Wan",
"Congying Xia",
"Lifang He",
"Philip Yu"
] | To capture the semantic graph structure from raw text, most existing summarization approaches are built on GNNs with a pre-trained model. However, these methods suffer from cumbersome procedures and inefficient computations for long-text documents. To mitigate these issues, this paper proposes HetFormer, a Transformer-... | 2021.emnlp-main.13 | 10.18653/v1/2021.emnlp-main.13 | null | 2110.06388 | title_snapshot |
2021.emnlp-main.14 | Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context | https://aclanthology.org/2021.emnlp-main.14/ | [
"Xinnian Liang",
"Shuangzhi Wu",
"Mu Li",
"Zhoujun Li"
] | Embedding based methods are widely used for unsupervised keyphrase extraction (UKE) tasks. Generally, these methods simply calculate similarities between phrase embeddings and document embedding, which is insufficient to capture different context for a more effective UKE model. In this paper, we propose a novel method ... | 2021.emnlp-main.14 | 10.18653/v1/2021.emnlp-main.14 | null | 2109.07293 | title_snapshot |
2021.emnlp-main.15 | Distantly Supervised Relation Extraction using Multi-Layer Revision Network and Confidence-based Multi-Instance Learning | https://aclanthology.org/2021.emnlp-main.15/ | [
"Xiangyu Lin",
"Tianyi Liu",
"Weijia Jia",
"Zhiguo Gong"
] | Distantly supervised relation extraction is widely used in the construction of knowledge bases due to its high efficiency. However, the automatically obtained instances are of low quality with numerous irrelevant words. In addition, the strong assumption of distant supervision leads to the existence of noisy sentences ... | 2021.emnlp-main.15 | 10.18653/v1/2021.emnlp-main.15 | null | null | null |
2021.emnlp-main.16 | Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification | https://aclanthology.org/2021.emnlp-main.16/ | [
"Qi Shi",
"Yu Zhang",
"Qingyu Yin",
"Ting Liu"
] | Table-based fact verification task aims to verify whether the given statement is supported by the given semi-structured table. Symbolic reasoning with logical operations plays a crucial role in this task. Existing methods leverage programs that contain rich logical information to enhance the verification process. Howev... | 2021.emnlp-main.16 | 10.18653/v1/2021.emnlp-main.16 | null | 2109.06480 | title_snapshot |
2021.emnlp-main.17 | A Partition Filter Network for Joint Entity and Relation Extraction | https://aclanthology.org/2021.emnlp-main.17/ | [
"Zhiheng Yan",
"Chong Zhang",
"Jinlan Fu",
"Qi Zhang",
"Zhongyu Wei"
] | In joint entity and relation extraction, existing work either sequentially encode task-specific features, leading to an imbalance in inter-task feature interaction where features extracted later have no direct contact with those that come first. Or they encode entity features and relation features in a parallel manner,... | 2021.emnlp-main.17 | 10.18653/v1/2021.emnlp-main.17 | null | 2108.12202 | title_snapshot |
2021.emnlp-main.18 | TEBNER: Domain Specific Named Entity Recognition with Type Expanded Boundary-aware Network | https://aclanthology.org/2021.emnlp-main.18/ | [
"Zheng Fang",
"Yanan Cao",
"Tai Li",
"Ruipeng Jia",
"Fang Fang",
"Yanmin Shang",
"Yuhai Lu"
] | To alleviate label scarcity in Named Entity Recognition (NER) task, distantly supervised NER methods are widely applied to automatically label data and identify entities. Although the human effort is reduced, the generated incomplete and noisy annotations pose new challenges for learning effective neural models. In thi... | 2021.emnlp-main.18 | 10.18653/v1/2021.emnlp-main.18 | null | null | null |
2021.emnlp-main.19 | Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge | https://aclanthology.org/2021.emnlp-main.19/ | [
"Bin Liang",
"Hang Su",
"Rongdi Yin",
"Lin Gui",
"Min Yang",
"Qin Zhao",
"Xiaoqi Yu",
"Ruifeng Xu"
] | In this paper, we investigate the Aspect Category Sentiment Analysis (ACSA) task from a novel perspective by exploring a Beta Distribution guided aspect-aware graph construction based on external knowledge. That is, we are no longer entangled about how to laboriously search the sentiment clues for coarse-grained aspect... | 2021.emnlp-main.19 | 10.18653/v1/2021.emnlp-main.19 | null | null | null |
2021.emnlp-main.20 | DILBERT: Customized Pre-Training for Domain Adaptation with Category Shift, with an Application to Aspect Extraction | https://aclanthology.org/2021.emnlp-main.20/ | [
"Entony Lekhtman",
"Yftah Ziser",
"Roi Reichart"
] | The rise of pre-trained language models has yielded substantial progress in the vast majority of Natural Language Processing (NLP) tasks. However, a generic approach towards the pre-training procedure can naturally be sub-optimal in some cases. Particularly, fine-tuning a pre-trained language model on a source domain a... | 2021.emnlp-main.20 | 10.18653/v1/2021.emnlp-main.20 | null | 2109.00571 | title_judge |
2021.emnlp-main.21 | Improving Multimodal fusion via Mutual Dependency Maximisation | https://aclanthology.org/2021.emnlp-main.21/ | [
"Pierre Colombo",
"Emile Chapuis",
"Matthieu Labeau",
"Chloé Clavel"
] | Multimodal sentiment analysis is a trending area of research, and multimodal fusion is one of its most active topic. Acknowledging humans communicate through a variety of channels (i.e visual, acoustic, linguistic), multimodal systems aim at integrating different unimodal representations into a synthetic one. So far, a... | 2021.emnlp-main.21 | 10.18653/v1/2021.emnlp-main.21 | null | 2109.00922 | title_snapshot |
2021.emnlp-main.22 | Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training | https://aclanthology.org/2021.emnlp-main.22/ | [
"Zhengyan Li",
"Yicheng Zou",
"Chong Zhang",
"Qi Zhang",
"Zhongyu Wei"
] | Aspect-based sentiment analysis aims to identify the sentiment polarity of a specific aspect in product reviews. We notice that about 30% of reviews do not contain obvious opinion words, but still convey clear human-aware sentiment orientation, which is known as implicit sentiment. However, recent neural network-based ... | 2021.emnlp-main.22 | 10.18653/v1/2021.emnlp-main.22 | null | 2111.02194 | title_snapshot |
2021.emnlp-main.23 | Progressive Self-Training with Discriminator for Aspect Term Extraction | https://aclanthology.org/2021.emnlp-main.23/ | [
"Qianlong Wang",
"Zhiyuan Wen",
"Qin Zhao",
"Min Yang",
"Ruifeng Xu"
] | Aspect term extraction aims to extract aspect terms from a review sentence that users have expressed opinions on. One of the remaining challenges for aspect term extraction resides in the lack of sufficient annotated data. While self-training is potentially an effective method to address this issue, the pseudo-labels i... | 2021.emnlp-main.23 | 10.18653/v1/2021.emnlp-main.23 | null | null | null |
2021.emnlp-main.24 | Reinforced Counterfactual Data Augmentation for Dual Sentiment Classification | https://aclanthology.org/2021.emnlp-main.24/ | [
"Hao Chen",
"Rui Xia",
"Jianfei Yu"
] | Data augmentation and adversarial perturbation approaches have recently achieved promising results in solving the over-fitting problem in many natural language processing (NLP) tasks including sentiment classification. However, existing studies aimed to improve the generalization ability by augmenting the training data... | 2021.emnlp-main.24 | 10.18653/v1/2021.emnlp-main.24 | null | null | null |
2021.emnlp-main.25 | Idiosyncratic but not Arbitrary: Learning Idiolects in Online Registers Reveals Distinctive yet Consistent Individual Styles | https://aclanthology.org/2021.emnlp-main.25/ | [
"Jian Zhu",
"David Jurgens"
] | An individual’s variation in writing style is often a function of both social and personal attributes. While structured social variation has been extensively studied, e.g., gender based variation, far less is known about how to characterize individual styles due to their idiosyncratic nature. We introduce a new approac... | 2021.emnlp-main.25 | 10.18653/v1/2021.emnlp-main.25 | null | 2109.03158 | title_snapshot |
2021.emnlp-main.26 | Narrative Theory for Computational Narrative Understanding | https://aclanthology.org/2021.emnlp-main.26/ | [
"Andrew Piper",
"Richard Jean So",
"David Bamman"
] | Over the past decade, the field of natural language processing has developed a wide array of computational methods for reasoning about narrative, including summarization, commonsense inference, and event detection. While this work has brought an important empirical lens for examining narrative, it is by and large divor... | 2021.emnlp-main.26 | 10.18653/v1/2021.emnlp-main.26 | null | null | null |
2021.emnlp-main.27 | (Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys | https://aclanthology.org/2021.emnlp-main.27/ | [
"Kenneth Joseph",
"Sarah Shugars",
"Ryan Gallagher",
"Jon Green",
"Alexi Quintana Mathé",
"Zijian An",
"David Lazer"
] | Stance detection, which aims to determine whether an individual is for or against a target concept, promises to uncover public opinion from large streams of social media data. Yet even human annotation of social media content does not always capture “stance” as measured by public opinion polls. We demonstrate this by d... | 2021.emnlp-main.27 | 10.18653/v1/2021.emnlp-main.27 | null | 2109.01762 | title_snapshot |
2021.emnlp-main.28 | How Does Counterfactually Augmented Data Impact Models for Social Computing Constructs? | https://aclanthology.org/2021.emnlp-main.28/ | [
"Indira Sen",
"Mattia Samory",
"Fabian Flöck",
"Claudia Wagner",
"Isabelle Augenstein"
] | As NLP models are increasingly deployed in socially situated settings such as online abusive content detection, it is crucial to ensure that these models are robust. One way of improving model robustness is to generate counterfactually augmented data (CAD) for training models that can better learn to distinguish betwee... | 2021.emnlp-main.28 | 10.18653/v1/2021.emnlp-main.28 | null | 2109.07022 | title_snapshot |
2021.emnlp-main.29 | Latent Hatred: A Benchmark for Understanding Implicit Hate Speech | https://aclanthology.org/2021.emnlp-main.29/ | [
"Mai ElSherief",
"Caleb Ziems",
"David Muchlinski",
"Vaishnavi Anupindi",
"Jordyn Seybolt",
"Munmun De Choudhury",
"Diyi Yang"
] | Hate speech has grown significantly on social media, causing serious consequences for victims of all demographics. Despite much attention being paid to characterize and detect discriminatory speech, most work has focused on explicit or overt hate speech, failing to address a more pervasive form based on coded or indire... | 2021.emnlp-main.29 | 10.18653/v1/2021.emnlp-main.29 | null | 2109.05322 | title_snapshot |
2021.emnlp-main.30 | Distilling Linguistic Context for Language Model Compression | https://aclanthology.org/2021.emnlp-main.30/ | [
"Geondo Park",
"Gyeongman Kim",
"Eunho Yang"
] | A computationally expensive and memory intensive neural network lies behind the recent success of language representation learning. Knowledge distillation, a major technique for deploying such a vast language model in resource-scarce environments, transfers the knowledge on individual word representations learned witho... | 2021.emnlp-main.30 | 10.18653/v1/2021.emnlp-main.30 | null | 2109.08359 | title_snapshot |
2021.emnlp-main.31 | Dynamic Knowledge Distillation for Pre-trained Language Models | https://aclanthology.org/2021.emnlp-main.31/ | [
"Lei Li",
"Yankai Lin",
"Shuhuai Ren",
"Peng Li",
"Jie Zhou",
"Xu Sun"
] | Knowledge distillation (KD) has been proved effective for compressing large-scale pre-trained language models. However, existing methods conduct KD statically, e.g., the student model aligns its output distribution to that of a selected teacher model on the pre-defined training dataset. In this paper, we explore whethe... | 2021.emnlp-main.31 | 10.18653/v1/2021.emnlp-main.31 | null | 2109.11295 | title_snapshot |
2021.emnlp-main.32 | Few-Shot Text Generation with Natural Language Instructions | https://aclanthology.org/2021.emnlp-main.32/ | [
"Timo Schick",
"Hinrich Schütze"
] | Providing pretrained language models with simple task descriptions in natural language enables them to solve some tasks in a fully unsupervised fashion. Moreover, when combined with regular learning from examples, this idea yields impressive few-shot results for a wide range of text classification tasks. It is also a p... | 2021.emnlp-main.32 | 10.18653/v1/2021.emnlp-main.32 | null | null | null |
2021.emnlp-main.33 | SOM-NCSCM : An Efficient Neural Chinese Sentence Compression Model Enhanced with Self-Organizing Map | https://aclanthology.org/2021.emnlp-main.33/ | [
"Kangli Zi",
"Shi Wang",
"Yu Liu",
"Jicun Li",
"Yanan Cao",
"Cungen Cao"
] | Sentence Compression (SC), which aims to shorten sentences while retaining important words that express the essential meanings, has been studied for many years in many languages, especially in English. However, improvements on Chinese SC task are still quite few due to several difficulties: scarce of parallel corpora, ... | 2021.emnlp-main.33 | 10.18653/v1/2021.emnlp-main.33 | null | null | null |
2021.emnlp-main.34 | Efficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data by Feature Similarity | https://aclanthology.org/2021.emnlp-main.34/ | [
"Po-Nien Kung",
"Sheng-Siang Yin",
"Yi-Cheng Chen",
"Tse-Hsuan Yang",
"Yun-Nung Chen"
] | Multi-task auxiliary learning utilizes a set of relevant auxiliary tasks to improve the performance of a primary task. A common usage is to manually select multiple auxiliary tasks for multi-task learning on all data, which raises two issues: (1) selecting beneficial auxiliary tasks for a primary task is nontrivial; (2... | 2021.emnlp-main.34 | 10.18653/v1/2021.emnlp-main.34 | null | null | null |
2021.emnlp-main.35 | GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation | https://aclanthology.org/2021.emnlp-main.35/ | [
"Derek Chen",
"Zhou Yu"
] | Practical dialogue systems require robust methods of detecting out-of-scope (OOS) utterances to avoid conversational breakdowns and related failure modes. Directly training a model with labeled OOS examples yields reasonable performance, but obtaining such data is a resource-intensive process. To tackle this limited-da... | 2021.emnlp-main.35 | 10.18653/v1/2021.emnlp-main.35 | null | 2109.03079 | title_snapshot |
2021.emnlp-main.36 | Graph Based Network with Contextualized Representations of Turns in Dialogue | https://aclanthology.org/2021.emnlp-main.36/ | [
"Bongseok Lee",
"Yong Suk Choi"
] | Dialogue-based relation extraction (RE) aims to extract relation(s) between two arguments that appear in a dialogue. Because dialogues have the characteristics of high personal pronoun occurrences and low information density, and since most relational facts in dialogues are not supported by any single sentence, dialogu... | 2021.emnlp-main.36 | 10.18653/v1/2021.emnlp-main.36 | null | 2109.04008 | title_snapshot |
2021.emnlp-main.37 | Automatically Exposing Problems with Neural Dialog Models | https://aclanthology.org/2021.emnlp-main.37/ | [
"Dian Yu",
"Kenji Sagae"
] | Neural dialog models are known to suffer from problems such as generating unsafe and inconsistent responses. Even though these problems are crucial and prevalent, they are mostly manually identified by model designers through interactions. Recently, some research instructs crowdworkers to goad the bots into triggering ... | 2021.emnlp-main.37 | 10.18653/v1/2021.emnlp-main.37 | null | 2109.06950 | title_snapshot |
2021.emnlp-main.38 | Event Coreference Data (Almost) for Free: Mining Hyperlinks from Online News | https://aclanthology.org/2021.emnlp-main.38/ | [
"Michael Bugert",
"Iryna Gurevych"
] | Cross-document event coreference resolution (CDCR) is the task of identifying which event mentions refer to the same events throughout a collection of documents. Annotating CDCR data is an arduous and expensive process, explaining why existing corpora are small and lack domain coverage. To overcome this bottleneck, we ... | 2021.emnlp-main.38 | 10.18653/v1/2021.emnlp-main.38 | null | null | null |
2021.emnlp-main.39 | Inducing Stereotypical Character Roles from Plot Structure | https://aclanthology.org/2021.emnlp-main.39/ | [
"Labiba Jahan",
"Rahul Mittal",
"Mark Finlayson"
] | Stereotypical character roles-also known as archetypes or dramatis personae-play an important function in narratives: they facilitate efficient communication with bundles of default characteristics and associations and ease understanding of those characters’ roles in the overall narrative. We present a fully unsupervis... | 2021.emnlp-main.39 | 10.18653/v1/2021.emnlp-main.39 | null | null | null |
2021.emnlp-main.40 | Multitask Semi-Supervised Learning for Class-Imbalanced Discourse Classification | https://aclanthology.org/2021.emnlp-main.40/ | [
"Alexander Spangher",
"Jonathan May",
"Sz-Rung Shiang",
"Lingjia Deng"
] | As labeling schemas evolve over time, small differences can render datasets following older schemas unusable. This prevents researchers from building on top of previous annotation work and results in the existence, in discourse learning in particular, of many small class-imbalanced datasets. In this work, we show that ... | 2021.emnlp-main.40 | 10.18653/v1/2021.emnlp-main.40 | null | 2101.00389 | title_judge |
2021.emnlp-main.41 | Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models | https://aclanthology.org/2021.emnlp-main.41/ | [
"Robert Wolfe",
"Aylin Caliskan"
] | We use a dataset of U.S. first names with labels based on predominant gender and racial group to examine the effect of training corpus frequency on tokenization, contextualization, similarity to initial representation, and bias in BERT, GPT-2, T5, and XLNet. We show that predominantly female and non-white names are les... | 2021.emnlp-main.41 | 10.18653/v1/2021.emnlp-main.41 | null | 2110.00672 | title_snapshot |
2021.emnlp-main.42 | Mitigating Language-Dependent Ethnic Bias in BERT | https://aclanthology.org/2021.emnlp-main.42/ | [
"Jaimeen Ahn",
"Alice Oh"
] | In this paper, we study ethnic bias and how it varies across languages by analyzing and mitigating ethnic bias in monolingual BERT for English, German, Spanish, Korean, Turkish, and Chinese. To observe and quantify ethnic bias, we develop a novel metric called Categorical Bias score. Then we propose two methods for mit... | 2021.emnlp-main.42 | 10.18653/v1/2021.emnlp-main.42 | null | 2109.05704 | title_snapshot |
2021.emnlp-main.43 | Adversarial Scrubbing of Demographic Information for Text Classification | https://aclanthology.org/2021.emnlp-main.43/ | [
"Somnath Basu Roy Chowdhury",
"Sayan Ghosh",
"Yiyuan Li",
"Junier Oliva",
"Shashank Srivastava",
"Snigdha Chaturvedi"
] | Contextual representations learned by language models can often encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated target task. We aim to scrub such undesirable attributes and learn fair representations while maintaining performance on the target task. In thi... | 2021.emnlp-main.43 | 10.18653/v1/2021.emnlp-main.43 | null | 2109.08613 | title_snapshot |
2021.emnlp-main.44 | Open-domain clarification question generation without question examples | https://aclanthology.org/2021.emnlp-main.44/ | [
"Julia White",
"Gabriel Poesia",
"Robert Hawkins",
"Dorsa Sadigh",
"Noah Goodman"
] | An overarching goal of natural language processing is to enable machines to communicate seamlessly with humans. However, natural language can be ambiguous or unclear. In cases of uncertainty, humans engage in an interactive process known as repair: asking questions and seeking clarification until their uncertainty is r... | 2021.emnlp-main.44 | 10.18653/v1/2021.emnlp-main.44 | null | 2110.09779 | title_snapshot |
2021.emnlp-main.45 | Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting | https://aclanthology.org/2021.emnlp-main.45/ | [
"Wangchunshu Zhou",
"Tao Ge",
"Canwen Xu",
"Ke Xu",
"Furu Wei"
] | In this paper, we propose Sequence Span Rewriting (SSR), a self-supervised task for sequence-to-sequence (Seq2Seq) pre-training. SSR learns to refine the machine-generated imperfect text spans into ground truth text. SSR provides more fine-grained and informative supervision in addition to the original text-infilling o... | 2021.emnlp-main.45 | 10.18653/v1/2021.emnlp-main.45 | null | 2101.00416 | title_snapshot |
2021.emnlp-main.46 | Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data | https://aclanthology.org/2021.emnlp-main.46/ | [
"Dheeraj Mekala",
"Varun Gangal",
"Jingbo Shang"
] | Existing text classification methods mainly focus on a fixed label set, whereas many real-world applications require extending to new fine-grained classes as the number of samples per label increases. To accommodate such requirements, we introduce a new problem called coarse-to-fine grained classification, which aims t... | 2021.emnlp-main.46 | 10.18653/v1/2021.emnlp-main.46 | null | 2109.10856 | title_snapshot |
2021.emnlp-main.47 | Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries | https://aclanthology.org/2021.emnlp-main.47/ | [
"Carl Edwards",
"ChengXiang Zhai",
"Heng Ji"
] | We propose a new task, Text2Mol, to retrieve molecules using natural language descriptions as queries. Natural language and molecules encode information in very different ways, which leads to the exciting but challenging problem of integrating these two very different modalities. Although some work has been done on tex... | 2021.emnlp-main.47 | 10.18653/v1/2021.emnlp-main.47 | null | null | null |
2021.emnlp-main.48 | Classification of hierarchical text using geometric deep learning: the case of clinical trials corpus | https://aclanthology.org/2021.emnlp-main.48/ | [
"Sohrab Ferdowsi",
"Nikolay Borissov",
"Julien Knafou",
"Poorya Amini",
"Douglas Teodoro"
] | We consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories. While graph neural networks can efficiently handle the variable structure of hierarchical documents using the permutation invariant message passing operations, we show that we c... | 2021.emnlp-main.48 | 10.18653/v1/2021.emnlp-main.48 | null | 2110.15710 | title_snapshot |
2021.emnlp-main.49 | The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers | https://aclanthology.org/2021.emnlp-main.49/ | [
"Róbert Csordás",
"Kazuki Irie",
"Juergen Schmidhuber"
] | Recently, many datasets have been proposed to test the systematic generalization ability of neural networks. The companion baseline Transformers, typically trained with default hyper-parameters from standard tasks, are shown to fail dramatically. Here we demonstrate that by revisiting model configurations as basic as s... | 2021.emnlp-main.49 | 10.18653/v1/2021.emnlp-main.49 | null | 2108.12284 | title_snapshot |
2021.emnlp-main.50 | Artificial Text Detection via Examining the Topology of Attention Maps | https://aclanthology.org/2021.emnlp-main.50/ | [
"Laida Kushnareva",
"Daniil Cherniavskii",
"Vladislav Mikhailov",
"Ekaterina Artemova",
"Serguei Barannikov",
"Alexander Bernstein",
"Irina Piontkovskaya",
"Dmitri Piontkovski",
"Evgeny Burnaev"
] | The impressive capabilities of recent generative models to create texts that are challenging to distinguish from the human-written ones can be misused for generating fake news, product reviews, and even abusive content. Despite the prominent performance of existing methods for artificial text detection, they still lack... | 2021.emnlp-main.50 | 10.18653/v1/2021.emnlp-main.50 | null | 2109.04825 | title_snapshot |
2021.emnlp-main.51 | Active Learning by Acquiring Contrastive Examples | https://aclanthology.org/2021.emnlp-main.51/ | [
"Katerina Margatina",
"Giorgos Vernikos",
"Loïc Barrault",
"Nikolaos Aletras"
] | Common acquisition functions for active learning use either uncertainty or diversity sampling, aiming to select difficult and diverse data points from the pool of unlabeled data, respectively. In this work, leveraging the best of both worlds, we propose an acquisition function that opts for selecting contrastive exampl... | 2021.emnlp-main.51 | 10.18653/v1/2021.emnlp-main.51 | null | 2109.03764 | title_snapshot |
2021.emnlp-main.52 | Conditional Poisson Stochastic Beam Search | https://aclanthology.org/2021.emnlp-main.52/ | [
"Clara Meister",
"Afra Amini",
"Tim Vieira",
"Ryan Cotterell"
] | Beam search is the default decoding strategy for many sequence generation tasks in NLP. The set of approximate K-best items returned by the algorithm is a useful summary of the distribution for many applications; however, the candidates typically exhibit high overlap and may give a highly biased estimate for expectatio... | 2021.emnlp-main.52 | 10.18653/v1/2021.emnlp-main.52 | null | 2109.11034 | title_snapshot |
2021.emnlp-main.53 | Building Adaptive Acceptability Classifiers for Neural NLG | https://aclanthology.org/2021.emnlp-main.53/ | [
"Soumya Batra",
"Shashank Jain",
"Peyman Heidari",
"Ankit Arun",
"Catharine Youngs",
"Xintong Li",
"Pinar Donmez",
"Shawn Mei",
"Shiunzu Kuo",
"Vikas Bhardwaj",
"Anuj Kumar",
"Michael White"
] | We propose a novel framework to train models to classify acceptability of responses generated by natural language generation (NLG) models, improving upon existing sentence transformation and model-based approaches. An NLG response is considered acceptable if it is both semantically correct and grammatical. We don’t mak... | 2021.emnlp-main.53 | 10.18653/v1/2021.emnlp-main.53 | null | null | null |
2021.emnlp-main.54 | Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences | https://aclanthology.org/2021.emnlp-main.54/ | [
"Denis Emelin",
"Ronan Le Bras",
"Jena D. Hwang",
"Maxwell Forbes",
"Yejin Choi"
] | In social settings, much of human behavior is governed by unspoken rules of conduct rooted in societal norms. For artificial systems to be fully integrated into social environments, adherence to such norms is a central prerequisite. To investigate whether language generation models can serve as behavioral priors for sy... | 2021.emnlp-main.54 | 10.18653/v1/2021.emnlp-main.54 | null | 2012.15738 | title_snapshot |
2021.emnlp-main.55 | Truth-Conditional Captions for Time Series Data | https://aclanthology.org/2021.emnlp-main.55/ | [
"Harsh Jhamtani",
"Taylor Berg-Kirkpatrick"
] | In this paper, we explore the task of automatically generating natural language descriptions of salient patterns in a time series, such as stock prices of a company over a week. A model for this task should be able to extract high-level patterns such as presence of a peak or a dip. While typical contemporary neural mod... | 2021.emnlp-main.55 | 10.18653/v1/2021.emnlp-main.55 | null | null | null |
2021.emnlp-main.56 | Injecting Entity Types into Entity-Guided Text Generation | https://aclanthology.org/2021.emnlp-main.56/ | [
"Xiangyu Dong",
"Wenhao Yu",
"Chenguang Zhu",
"Meng Jiang"
] | Recent successes in deep generative modeling have led to significant advances in natural language generation (NLG). Incorporating entities into neural generation models has demonstrated great improvements by assisting to infer the summary topic and to generate coherent content. To enhance the role of entity in NLG, in ... | 2021.emnlp-main.56 | 10.18653/v1/2021.emnlp-main.56 | null | 2009.13401 | title_snapshot |
2021.emnlp-main.57 | Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation | https://aclanthology.org/2021.emnlp-main.57/ | [
"Leonardo F. R. Ribeiro",
"Jonas Pfeiffer",
"Yue Zhang",
"Iryna Gurevych"
] | Recent work on multilingual AMR-to-text generation has exclusively focused on data augmentation strategies that utilize silver AMR. However, this assumes a high quality of generated AMRs, potentially limiting the transferability to the target task. In this paper, we investigate different techniques for automatically ge... | 2021.emnlp-main.57 | 10.18653/v1/2021.emnlp-main.57 | null | 2109.03808 | title_snapshot |
2021.emnlp-main.58 | Learning Compact Metrics for MT | https://aclanthology.org/2021.emnlp-main.58/ | [
"Amy Pu",
"Hyung Won Chung",
"Ankur Parikh",
"Sebastian Gehrmann",
"Thibault Sellam"
] | Recent developments in machine translation and multilingual text generation have led researchers to adopt trained metrics such as COMET or BLEURT, which treat evaluation as a regression problem and use representations from multilingual pre-trained models such as XLM-RoBERTa or mBERT. Yet studies on related tasks sugges... | 2021.emnlp-main.58 | 10.18653/v1/2021.emnlp-main.58 | null | 2110.06341 | title_snapshot |
2021.emnlp-main.59 | The Impact of Positional Encodings on Multilingual Compression | https://aclanthology.org/2021.emnlp-main.59/ | [
"Vinit Ravishankar",
"Anders Søgaard"
] | In order to preserve word-order information in a non-autoregressive setting, transformer architectures tend to include positional knowledge, by (for instance) adding positional encodings to token embeddings. Several modifications have been proposed over the sinusoidal positional encodings used in the original transform... | 2021.emnlp-main.59 | 10.18653/v1/2021.emnlp-main.59 | null | 2109.05388 | title_snapshot |
2021.emnlp-main.60 | Disentangling Representations of Text by Masking Transformers | https://aclanthology.org/2021.emnlp-main.60/ | [
"Xiongyi Zhang",
"Jan-Willem van de Meent",
"Byron Wallace"
] | Representations from large pretrained models such as BERT encode a range of features into monolithic vectors, affording strong predictive accuracy across a range of downstream tasks. In this paper we explore whether it is possible to learn disentangled representations by identifying existing subnetworks within pretrain... | 2021.emnlp-main.60 | 10.18653/v1/2021.emnlp-main.60 | null | 2104.07155 | title_snapshot |
2021.emnlp-main.61 | Exploring the Role of BERT Token Representations to Explain Sentence Probing Results | https://aclanthology.org/2021.emnlp-main.61/ | [
"Hosein Mohebbi",
"Ali Modarressi",
"Mohammad Taher Pilehvar"
] | Several studies have been carried out on revealing linguistic features captured by BERT. This is usually achieved by training a diagnostic classifier on the representations obtained from different layers of BERT. The subsequent classification accuracy is then interpreted as the ability of the model in encoding the corr... | 2021.emnlp-main.61 | 10.18653/v1/2021.emnlp-main.61 | null | 2104.01477 | title_snapshot |
2021.emnlp-main.62 | Do Long-Range Language Models Actually Use Long-Range Context? | https://aclanthology.org/2021.emnlp-main.62/ | [
"Simeng Sun",
"Kalpesh Krishna",
"Andrew Mattarella-Micke",
"Mohit Iyyer"
] | Language models are generally trained on short, truncated input sequences, which limits their ability to use discourse-level information present in long-range context to improve their predictions. Recent efforts to improve the efficiency of self-attention have led to a proliferation of long-range Transformer language m... | 2021.emnlp-main.62 | 10.18653/v1/2021.emnlp-main.62 | null | 2109.09115 | title_snapshot |
2021.emnlp-main.63 | The World of an Octopus: How Reporting Bias Influences a Language Model’s Perception of Color | https://aclanthology.org/2021.emnlp-main.63/ | [
"Cory Paik",
"Stéphane Aroca-Ouellette",
"Alessandro Roncone",
"Katharina Kann"
] | Recent work has raised concerns about the inherent limitations of text-only pretraining. In this paper, we first demonstrate that reporting bias, the tendency of people to not state the obvious, is one of the causes of this limitation, and then investigate to what extent multimodal training can mitigate this issue. To ... | 2021.emnlp-main.63 | 10.18653/v1/2021.emnlp-main.63 | null | 2110.08182 | title_snapshot |
2021.emnlp-main.64 | SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers | https://aclanthology.org/2021.emnlp-main.64/ | [
"Dheeraj Rajagopal",
"Vidhisha Balachandran",
"Eduard H Hovy",
"Yulia Tsvetkov"
] | We introduce SelfExplain, a novel self-explaining model that explains a text classifier’s predictions using phrase-based concepts. SelfExplain augments existing neural classifiers by adding (1) a globally interpretable layer that identifies the most influential concepts in the training set for a given sample and (2) a ... | 2021.emnlp-main.64 | 10.18653/v1/2021.emnlp-main.64 | null | 2103.12279 | title_snapshot |
2021.emnlp-main.65 | Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories | https://aclanthology.org/2021.emnlp-main.65/ | [
"David Wilmot",
"Frank Keller"
] | Measuring event salience is essential in the understanding of stories. This paper takes a recent unsupervised method for salience detection derived from Barthes Cardinal Functions and theories of surprise and applies it to longer narrative forms. We improve the standard transformer language model by incorporating an ex... | 2021.emnlp-main.65 | 10.18653/v1/2021.emnlp-main.65 | null | 2109.03754 | title_snapshot |
2021.emnlp-main.66 | Semantic Novelty Detection in Natural Language Descriptions | https://aclanthology.org/2021.emnlp-main.66/ | [
"Nianzu Ma",
"Alexander Politowicz",
"Sahisnu Mazumder",
"Jiahua Chen",
"Bing Liu",
"Eric Robertson",
"Scott Grigsby"
] | This paper proposes to study a fine-grained semantic novelty detection task, which can be illustrated with the following example. It is normal that a person walks a dog in the park, but if someone says “A man is walking a chicken in the park”, it is novel. Given a set of natural language descriptions of normal scenes, ... | 2021.emnlp-main.66 | 10.18653/v1/2021.emnlp-main.66 | null | null | null |
2021.emnlp-main.67 | Jump-Starting Item Parameters for Adaptive Language Tests | https://aclanthology.org/2021.emnlp-main.67/ | [
"Arya D. McCarthy",
"Kevin P. Yancey",
"Geoffrey T. LaFlair",
"Jesse Egbert",
"Manqian Liao",
"Burr Settles"
] | A challenge in designing high-stakes language assessments is calibrating the test item difficulties, either a priori or from limited pilot test data. While prior work has addressed ‘cold start’ estimation of item difficulties without piloting, we devise a multi-task generalized linear model with BERT features to jump-s... | 2021.emnlp-main.67 | 10.18653/v1/2021.emnlp-main.67 | null | null | null |
2021.emnlp-main.68 | Voice Query Auto Completion | https://aclanthology.org/2021.emnlp-main.68/ | [
"Raphael Tang",
"Karun Kumar",
"Kendra Chalkley",
"Ji Xin",
"Liming Zhang",
"Wenyan Li",
"Gefei Yang",
"Yajie Mao",
"Junho Shin",
"Geoffrey Craig Murray",
"Jimmy Lin"
] | Query auto completion (QAC) is the task of predicting a search engine user’s final query from their intermediate, incomplete query. In this paper, we extend QAC to the streaming voice search setting, where automatic speech recognition systems produce intermediate transcriptions as users speak. Naively applying existing... | 2021.emnlp-main.68 | 10.18653/v1/2021.emnlp-main.68 | null | null | null |
2021.emnlp-main.69 | CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification | https://aclanthology.org/2021.emnlp-main.69/ | [
"Matúš Falis",
"Hang Dong",
"Alexandra Birch",
"Beatrice Alex"
] | Large-Scale Multi-Label Text Classification (LMTC) includes tasks with hierarchical label spaces, such as automatic assignment of ICD-9 codes to discharge summaries. Performance of models in prior art is evaluated with standard precision, recall, and F1 measures without regard for the rich hierarchical structure. In th... | 2021.emnlp-main.69 | 10.18653/v1/2021.emnlp-main.69 | null | 2109.04853 | title_snapshot |
2021.emnlp-main.70 | Learning Universal Authorship Representations | https://aclanthology.org/2021.emnlp-main.70/ | [
"Rafael A. Rivera-Soto",
"Olivia Elizabeth Miano",
"Juanita Ordonez",
"Barry Y. Chen",
"Aleem Khan",
"Marcus Bishop",
"Nicholas Andrews"
] | Determining whether two documents were composed by the same author, also known as authorship verification, has traditionally been tackled using statistical methods. Recently, authorship representations learned using neural networks have been found to outperform alternatives, particularly in large-scale settings involvi... | 2021.emnlp-main.70 | 10.18653/v1/2021.emnlp-main.70 | null | null | null |
2021.emnlp-main.71 | Predicting emergent linguistic compositions through time: Syntactic frame extension via multimodal chaining | https://aclanthology.org/2021.emnlp-main.71/ | [
"Lei Yu",
"Yang Xu"
] | Natural language relies on a finite lexicon to express an unbounded set of emerging ideas. One result of this tension is the formation of new compositions, such that existing linguistic units can be combined with emerging items into novel expressions. We develop a framework that exploits the cognitive mechanisms of cha... | 2021.emnlp-main.71 | 10.18653/v1/2021.emnlp-main.71 | null | 2109.04652 | title_snapshot |
2021.emnlp-main.72 | Frequency Effects on Syntactic Rule Learning in Transformers | https://aclanthology.org/2021.emnlp-main.72/ | [
"Jason Wei",
"Dan Garrette",
"Tal Linzen",
"Ellie Pavlick"
] | Pre-trained language models perform well on a variety of linguistic tasks that require symbolic reasoning, raising the question of whether such models implicitly represent abstract symbols and rules. We investigate this question using the case study of BERT’s performance on English subject–verb agreement. Unlike prior ... | 2021.emnlp-main.72 | 10.18653/v1/2021.emnlp-main.72 | null | 2109.07020 | title_snapshot |
2021.emnlp-main.73 | A surprisal–duration trade-off across and within the world’s languages | https://aclanthology.org/2021.emnlp-main.73/ | [
"Tiago Pimentel",
"Clara Meister",
"Elizabeth Salesky",
"Simone Teufel",
"Damián Blasi",
"Ryan Cotterell"
] | While there exist scores of natural languages, each with its unique features and idiosyncrasies, they all share a unifying theme: enabling human communication. We may thus reasonably predict that human cognition shapes how these languages evolve and are used. Assuming that the capacity to process information is roughly... | 2021.emnlp-main.73 | 10.18653/v1/2021.emnlp-main.73 | null | 2109.15000 | title_snapshot |
2021.emnlp-main.74 | Revisiting the Uniform Information Density Hypothesis | https://aclanthology.org/2021.emnlp-main.74/ | [
"Clara Meister",
"Tiago Pimentel",
"Patrick Haller",
"Lena Jäger",
"Ryan Cotterell",
"Roger Levy"
] | The uniform information density (UID) hypothesis posits a preference among language users for utterances structured such that information is distributed uniformly across a signal. While its implications on language production have been well explored, the hypothesis potentially makes predictions about language comprehen... | 2021.emnlp-main.74 | 10.18653/v1/2021.emnlp-main.74 | null | 2109.11635 | title_snapshot |
2021.emnlp-main.75 | Condenser: a Pre-training Architecture for Dense Retrieval | https://aclanthology.org/2021.emnlp-main.75/ | [
"Luyu Gao",
"Jamie Callan"
] | Pre-trained Transformer language models (LM) have become go-to text representation encoders. Prior research fine-tunes deep LMs to encode text sequences such as sentences and passages into single dense vector representations for efficient text comparison and retrieval. However, dense encoders require a lot of data and ... | 2021.emnlp-main.75 | 10.18653/v1/2021.emnlp-main.75 | null | 2104.08253 | title_snapshot |
2021.emnlp-main.76 | Monitoring geometrical properties of word embeddings for detecting the emergence of new topics. | https://aclanthology.org/2021.emnlp-main.76/ | [
"Clément Christophe",
"Julien Velcin",
"Jairo Cugliari",
"Manel Boumghar",
"Philippe Suignard"
] | Slow emerging topic detection is a task between event detection, where we aggregate behaviors of different words on short period of time, and language evolution, where we monitor their long term evolution. In this work, we tackle the problem of early detection of slowly emerging new topics. To this end, we gather evide... | 2021.emnlp-main.76 | 10.18653/v1/2021.emnlp-main.76 | null | 2111.03496 | title_snapshot |
2021.emnlp-main.77 | Contextualized Query Embeddings for Conversational Search | https://aclanthology.org/2021.emnlp-main.77/ | [
"Sheng-Chieh Lin",
"Jheng-Hong Yang",
"Jimmy Lin"
] | This paper describes a compact and effective model for low-latency passage retrieval in conversational search based on learned dense representations. Prior to our work, the state-of-the-art approach uses a multi-stage pipeline comprising conversational query reformulation and information retrieval modules. Despite its ... | 2021.emnlp-main.77 | 10.18653/v1/2021.emnlp-main.77 | null | 2104.08707 | title_snapshot |
2021.emnlp-main.78 | Ultra-High Dimensional Sparse Representations with Binarization for Efficient Text Retrieval | https://aclanthology.org/2021.emnlp-main.78/ | [
"Kyoung-Rok Jang",
"Junmo Kang",
"Giwon Hong",
"Sung-Hyon Myaeng",
"Joohee Park",
"Taewon Yoon",
"Heecheol Seo"
] | The semantic matching capabilities of neural information retrieval can ameliorate synonymy and polysemy problems of symbolic approaches. However, neural models’ dense representations are more suitable for re-ranking, due to their inefficiency. Sparse representations, either in symbolic or latent form, are more efficien... | 2021.emnlp-main.78 | 10.18653/v1/2021.emnlp-main.78 | null | 2104.07198 | title_snapshot |
2021.emnlp-main.79 | IR like a SIR: Sense-enhanced Information Retrieval for Multiple Languages | https://aclanthology.org/2021.emnlp-main.79/ | [
"Rexhina Blloshmi",
"Tommaso Pasini",
"Niccolò Campolungo",
"Somnath Banerjee",
"Roberto Navigli",
"Gabriella Pasi"
] | With the advent of contextualized embeddings, attention towards neural ranking approaches for Information Retrieval increased considerably. However, two aspects have remained largely neglected: i) queries usually consist of few keywords only, which increases ambiguity and makes their contextualization harder, and ii) p... | 2021.emnlp-main.79 | 10.18653/v1/2021.emnlp-main.79 | null | null | null |
2021.emnlp-main.80 | Neural Attention-Aware Hierarchical Topic Model | https://aclanthology.org/2021.emnlp-main.80/ | [
"Yuan Jin",
"He Zhao",
"Ming Liu",
"Lan Du",
"Wray Buntine"
] | Neural topic models (NTMs) apply deep neural networks to topic modelling. Despite their success, NTMs generally ignore two important aspects: (1) only document-level word count information is utilized for the training, while more fine-grained sentence-level information is ignored, and (2) external semantic knowledge re... | 2021.emnlp-main.80 | 10.18653/v1/2021.emnlp-main.80 | null | 2110.07161 | title_snapshot |
2021.emnlp-main.81 | Relational World Knowledge Representation in Contextual Language Models: A Review | https://aclanthology.org/2021.emnlp-main.81/ | [
"Tara Safavi",
"Danai Koutra"
] | Relational knowledge bases (KBs) are commonly used to represent world knowledge in machines. However, while advantageous for their high degree of precision and interpretability, KBs are usually organized according to manually-defined schemas, which limit their expressiveness and require significant human efforts to eng... | 2021.emnlp-main.81 | 10.18653/v1/2021.emnlp-main.81 | null | 2104.05837 | title_snapshot |
2021.emnlp-main.82 | Certified Robustness to Programmable Transformations in LSTMs | https://aclanthology.org/2021.emnlp-main.82/ | [
"Yuhao Zhang",
"Aws Albarghouthi",
"Loris D’Antoni"
] | Deep neural networks for natural language processing are fragile in the face of adversarial examples—small input perturbations, like synonym substitution or word duplication, which cause a neural network to change its prediction. We present an approach to certifying the robustness of LSTMs (and extensions of LSTMs) and... | 2021.emnlp-main.82 | 10.18653/v1/2021.emnlp-main.82 | null | 2102.07818 | title_snapshot |
2021.emnlp-main.83 | ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models | https://aclanthology.org/2021.emnlp-main.83/ | [
"Pierre Dognin",
"Inkit Padhi",
"Igor Melnyk",
"Payel Das"
] | Automatic construction of relevant Knowledge Bases (KBs) from text, and generation of semantically meaningful text from KBs are both long-standing goals in Machine Learning. In this paper, we present ReGen, a bidirectional generation of text and graph leveraging Reinforcement Learning to improve performance. Graph line... | 2021.emnlp-main.83 | 10.18653/v1/2021.emnlp-main.83 | null | 2108.12472 | title_snapshot |
2021.emnlp-main.84 | Contrastive Out-of-Distribution Detection for Pretrained Transformers | https://aclanthology.org/2021.emnlp-main.84/ | [
"Wenxuan Zhou",
"Fangyu Liu",
"Muhao Chen"
] | Pretrained Transformers achieve remarkable performance when training and test data are from the same distribution. However, in real-world scenarios, the model often faces out-of-distribution (OOD) instances that can cause severe semantic shift problems at inference time. Therefore, in practice, a reliable model should ... | 2021.emnlp-main.84 | 10.18653/v1/2021.emnlp-main.84 | null | 2104.08812 | title_snapshot |
2021.emnlp-main.85 | MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks | https://aclanthology.org/2021.emnlp-main.85/ | [
"Cristian-Paul Bara",
"Sky CH-Wang",
"Joyce Chai"
] | An ideal integration of autonomous agents in a human world implies that they are able to collaborate on human terms. In particular, theory of mind plays an important role in maintaining common ground during human collaboration and communication. To enable theory of mind modeling in situated interactions, we introduce a... | 2021.emnlp-main.85 | 10.18653/v1/2021.emnlp-main.85 | null | 2109.06275 | title_snapshot |
2021.emnlp-main.86 | Detecting Speaker Personas from Conversational Texts | https://aclanthology.org/2021.emnlp-main.86/ | [
"Jia-Chen Gu",
"Zhenhua Ling",
"Yu Wu",
"Quan Liu",
"Zhigang Chen",
"Xiaodan Zhu"
] | Personas are useful for dialogue response prediction. However, the personas used in current studies are pre-defined and hard to obtain before a conversation. To tackle this issue, we study a new task, named Speaker Persona Detection (SPD), which aims to detect speaker personas based on the plain conversational text. In... | 2021.emnlp-main.86 | 10.18653/v1/2021.emnlp-main.86 | null | 2109.01330 | title_snapshot |
2021.emnlp-main.87 | Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking | https://aclanthology.org/2021.emnlp-main.87/ | [
"Nikita Moghe",
"Mark Steedman",
"Alexandra Birch"
] | Recent progress in task-oriented neural dialogue systems is largely focused on a handful of languages, as annotation of training data is tedious and expensive. Machine translation has been used to make systems multilingual, but this can introduce a pipeline of errors. Another promising solution is using cross-lingual t... | 2021.emnlp-main.87 | 10.18653/v1/2021.emnlp-main.87 | null | 2109.13620 | title_snapshot |
2021.emnlp-main.88 | ConvFiT: Conversational Fine-Tuning of Pretrained Language Models | https://aclanthology.org/2021.emnlp-main.88/ | [
"Ivan Vulić",
"Pei-Hao Su",
"Samuel Coope",
"Daniela Gerz",
"Paweł Budzianowski",
"Iñigo Casanueva",
"Nikola Mrkšić",
"Tsung-Hsien Wen"
] | Transformer-based language models (LMs) pretrained on large text collections are proven to store a wealth of semantic knowledge. However, 1) they are not effective as sentence encoders when used off-the-shelf, and 2) thus typically lag behind conversationally pretrained (e.g., via response selection) encoders on conver... | 2021.emnlp-main.88 | 10.18653/v1/2021.emnlp-main.88 | null | 2109.10126 | title_snapshot |
2021.emnlp-main.89 | We’ve had this conversation before: A Novel Approach to Measuring Dialog Similarity | https://aclanthology.org/2021.emnlp-main.89/ | [
"Ofer Lavi",
"Ella Rabinovich",
"Segev Shlomov",
"David Boaz",
"Inbal Ronen",
"Ateret Anaby Tavor"
] | Dialog is a core building block of human natural language interactions. It contains multi-party utterances used to convey information from one party to another in a dynamic and evolving manner. The ability to compare dialogs is beneficial in many real world use cases, such as conversation analytics for contact center c... | 2021.emnlp-main.89 | 10.18653/v1/2021.emnlp-main.89 | null | 2110.05780 | title_snapshot |
2021.emnlp-main.90 | Towards Incremental Transformers: An Empirical Analysis of Transformer Models for Incremental NLU | https://aclanthology.org/2021.emnlp-main.90/ | [
"Patrick Kahardipraja",
"Brielen Madureira",
"David Schlangen"
] | Incremental processing allows interactive systems to respond based on partial inputs, which is a desirable property e.g. in dialogue agents. The currently popular Transformer architecture inherently processes sequences as a whole, abstracting away the notion of time. Recent work attempts to apply Transformers increment... | 2021.emnlp-main.90 | 10.18653/v1/2021.emnlp-main.90 | null | 2109.07364 | title_snapshot |
2021.emnlp-main.91 | Feedback Attribution for Counterfactual Bandit Learning in Multi-Domain Spoken Language Understanding | https://aclanthology.org/2021.emnlp-main.91/ | [
"Tobias Falke",
"Patrick Lehnen"
] | With counterfactual bandit learning, models can be trained based on positive and negative feedback received for historical predictions, with no labeled data needed. Such feedback is often available in real-world dialog systems, however, the modularized architecture commonly used in large-scale systems prevents the dire... | 2021.emnlp-main.91 | 10.18653/v1/2021.emnlp-main.91 | null | null | null |
2021.emnlp-main.92 | Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction | https://aclanthology.org/2021.emnlp-main.92/ | [
"Oscar Sainz",
"Oier Lopez de Lacalle",
"Gorka Labaka",
"Ander Barrena",
"Eneko Agirre"
] | Relation extraction systems require large amounts of labeled examples which are costly to annotate. In this work we reformulate relation extraction as an entailment task, with simple, hand-made, verbalizations of relations produced in less than 15 min per relation. The system relies on a pretrained textual entailment e... | 2021.emnlp-main.92 | 10.18653/v1/2021.emnlp-main.92 | null | 2109.03659 | title_snapshot |
2021.emnlp-main.93 | Extend, don’t rebuild: Phrasing conditional graph modification as autoregressive sequence labelling | https://aclanthology.org/2021.emnlp-main.93/ | [
"Leon Weber",
"Jannes Münchmeyer",
"Samuele Garda",
"Ulf Leser"
] | Deriving and modifying graphs from natural language text has become a versatile basis technology for information extraction with applications in many subfields, such as semantic parsing or knowledge graph construction. A recent work used this technique for modifying scene graphs (He et al. 2020), by first encoding the ... | 2021.emnlp-main.93 | 10.18653/v1/2021.emnlp-main.93 | null | null | null |
2021.emnlp-main.94 | Zero-Shot Information Extraction as a Unified Text-to-Triple Translation | https://aclanthology.org/2021.emnlp-main.94/ | [
"Chenguang Wang",
"Xiao Liu",
"Zui Chen",
"Haoyun Hong",
"Jie Tang",
"Dawn Song"
] | We cast a suite of information extraction tasks into a text-to-triple translation framework. Instead of solving each task relying on task-specific datasets and models, we formalize the task as a translation between task-specific input text and output triples. By taking the task-specific input, we enable a task-agnostic... | 2021.emnlp-main.94 | 10.18653/v1/2021.emnlp-main.94 | null | 2109.11171 | title_snapshot |
2021.emnlp-main.95 | Learning Logic Rules for Document-Level Relation Extraction | https://aclanthology.org/2021.emnlp-main.95/ | [
"Dongyu Ru",
"Changzhi Sun",
"Jiangtao Feng",
"Lin Qiu",
"Hao Zhou",
"Weinan Zhang",
"Yong Yu",
"Lei Li"
] | Document-level relation extraction aims to identify relations between entities in a whole document. Prior efforts to capture long-range dependencies have relied heavily on implicitly powerful representations learned through (graph) neural networks, which makes the model less transparent. To tackle this challenge, in th... | 2021.emnlp-main.95 | 10.18653/v1/2021.emnlp-main.95 | null | 2111.05407 | title_snapshot |
2021.emnlp-main.96 | A Large-Scale Dataset for Empathetic Response Generation | https://aclanthology.org/2021.emnlp-main.96/ | [
"Anuradha Welivita",
"Yubo Xie",
"Pearl Pu"
] | Recent development in NLP shows a strong trend towards refining pre-trained models with a domain-specific dataset. This is especially the case for response generation where emotion plays an important role. However, existing empathetic datasets remain small, delaying research efforts in this area, for example, the devel... | 2021.emnlp-main.96 | 10.18653/v1/2021.emnlp-main.96 | null | null | null |
2021.emnlp-main.97 | The Perils of Using Mechanical Turk to Evaluate Open-Ended Text Generation | https://aclanthology.org/2021.emnlp-main.97/ | [
"Marzena Karpinska",
"Nader Akoury",
"Mohit Iyyer"
] | Recent text generation research has increasingly focused on open-ended domains such as story and poetry generation. Because models built for such tasks are difficult to evaluate automatically, most researchers in the space justify their modeling choices by collecting crowdsourced human judgments of text quality (e.g., ... | 2021.emnlp-main.97 | 10.18653/v1/2021.emnlp-main.97 | null | 2109.06835 | title_snapshot |
2021.emnlp-main.98 | Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus | https://aclanthology.org/2021.emnlp-main.98/ | [
"Jesse Dodge",
"Maarten Sap",
"Ana Marasović",
"William Agnew",
"Gabriel Ilharco",
"Dirk Groeneveld",
"Margaret Mitchell",
"Matt Gardner"
] | Large language models have led to remarkable progress on many NLP tasks, and researchers are turning to ever-larger text corpora to train them. Some of the largest corpora available are made by scraping significant portions of the internet, and are frequently introduced with only minimal documentation. In this work we ... | 2021.emnlp-main.98 | 10.18653/v1/2021.emnlp-main.98 | null | 2104.08758 | title_snapshot |
2021.emnlp-main.99 | AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages | https://aclanthology.org/2021.emnlp-main.99/ | [
"Machel Reid",
"Junjie Hu",
"Graham Neubig",
"Yutaka Matsuo"
] | Reproducible benchmarks are crucial in driving progress of machine translation research. However, existing machine translation benchmarks have been mostly limited to high-resource or well-represented languages. Despite an increasing interest in low-resource machine translation, there are no standardized reproducible be... | 2021.emnlp-main.99 | 10.18653/v1/2021.emnlp-main.99 | null | 2109.04715 | title_snapshot |
2021.emnlp-main.100 | Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer | https://aclanthology.org/2021.emnlp-main.100/ | [
"Eleftheria Briakou",
"Sweta Agrawal",
"Joel Tetreault",
"Marine Carpuat"
] | While the field of style transfer (ST) has been growing rapidly, it has been hampered by a lack of standardized practices for automatic evaluation. In this paper, we evaluate leading automatic metrics on the oft-researched task of formality style transfer. Unlike previous evaluations, which focus solely on English, we ... | 2021.emnlp-main.100 | 10.18653/v1/2021.emnlp-main.100 | null | 2110.10668 | title_snapshot |
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