mtybilly/Instruct2DS huggingface dataset:
Word-level Textual Adversarial Attacking as Combinatorial Optimization
Authors: Fanchao Qi, Chenghao Yang, Zhiyuan Liu, Meng Zhang, Qun Liu, Maosong Sun Yuan Zang
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Adversarial attacks are carried out to reveal the vulnerability of deep neural networks. Textual adversarial attacking is challenging because text is discrete and a small perturbation can bring significant change to the original input. Word-level attacking, which can be regarded as a combinatorial optimization problem, is a well-studied class of textual attack methods. However, existing word-level attack models are far from perfect, largely because unsuitable search space reduction methods and inefficient optimization algorithms are employed. In this paper, we propose a novel attack model, which incorporates the sememe-based word substitution method and particle swarm optimization-based search algorithm to solve the two problems separately. We conduct exhaustive experiments to evaluate our attack model by attacking BiLSTM and BERT on three benchmark datasets. Experimental results demonstrate that our model consistently achieves much higher attack success rates and crafts more high-quality adversarial examples as compared to baseline methods. Also, further experiments show our model has higher transferability and can bring more robustness enhancement to victim models by adversarial training. All the code and data of this paper can be obtained on r̆lhttps://github.com/thunlp/SememePSO-Attack.
Benchmarking Multimodal Regex Synthesis with Complex Structures
Authors: Qiaochu Chen, Isil Dillig, Greg Durrett Xi Ye
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Existing datasets for regular expression (regex) generation from natural language are limited in complexity; compared to regex tasks that users post on StackOverflow, the regexes in these datasets are simple, and the language used to describe them is not diverse. We introduce StructuredRegex, a new regex synthesis dataset differing from prior ones in three aspects. First, to obtain structurally complex and realistic regexes, we generate the regexes using a probabilistic grammar with pre-defined macros observed from real-world StackOverflow posts. Second, to obtain linguistically diverse natural language descriptions, we show crowdworkers abstract depictions of the underlying regex and ask them to describe the pattern they see, rather than having them paraphrase synthetic language. Third, we augment each regex example with a collection of strings that are and are not matched by the ground truth regex, similar to how real users give examples. Our quantitative and qualitative analysis demonstrates the advantages of StructuredRegex over prior datasets. Further experimental results using various multimodal synthesis techniques highlight the challenge presented by our dataset, including non-local constraints and multi-modal inputs.
Curriculum Learning for Natural Language Understanding
Authors: Licheng Zhang, Zhendong Mao, Quan Wang, Hongtao Xie, Yongdong Zhang Benfeng Xu
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: With the great success of pre-trained language models, the pretrain-finetune paradigm now becomes the undoubtedly dominant solution for natural language understanding (NLU) tasks. At the fine-tune stage, target task data is usually introduced in a completely random order and treated equally. However, examples in NLU tasks can vary greatly in difficulty, and similar to human learning procedure, language models can benefit from an easy-to-difficult curriculum. Based on this idea, we propose our Curriculum Learning approach. By reviewing the trainset in a crossed way, we are able to distinguish easy examples from difficult ones, and arrange a curriculum for language models. Without any manual model architecture design or use of external data, our Curriculum Learning approach obtains significant and universal performance improvements on a wide range of NLU tasks.
Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language?
Authors: Koji Mineshima, Daisuke Bekki, Kentaro Inui Hitomi Yanaka
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Despite the success of language models using neural networks, it remains unclear to what extent neural models have the generalization ability to perform inferences. In this paper, we introduce a method for evaluating whether neural models can learn systematicity of monotonicity inference in natural language, namely, the regularity for performing arbitrary inferences with generalization on composition. We consider four aspects of monotonicity inferences and test whether the models can systematically interpret lexical and logical phenomena on different training/test splits. A series of experiments show that three neural models systematically draw inferences on unseen combinations of lexical and logical phenomena when the syntactic structures of the sentences are similar between the training and test sets. However, the performance of the models significantly decreases when the structures are slightly changed in the test set while retaining all vocabularies and constituents already appearing in the training set. This indicates that the generalization ability of neural models is limited to cases where the syntactic structures are nearly the same as those in the training set.
Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder
Authors: Duyu Tang, Nan Duan, Jian Yin, Daxin Jiang, Ming Zhou Daya Guo
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Generating inferential texts about an event in different perspectives requires reasoning over different contexts that the event occurs. Existing works usually ignore the context that is not explicitly provided, resulting in a context-independent semantic representation that struggles to support the generation. To address this, we propose an approach that automatically finds evidence for an event from a large text corpus, and leverages the evidence to guide the generation of inferential texts. Our approach works in an encoderdecoder manner and is equipped with Vector Quantised-Variational Autoencoder, where the encoder outputs representations from a distribution over discrete variables. Such discrete representations enable automatically selecting relevant evidence, which not only facilitates evidence-aware generation, but also provides a natural way to uncover rationales behind the generation. Our approach provides state-of-the-art performance on both Event2mind and Atomic datasets. More importantly, we find that with discrete representations, our model selectively uses evidence to generate different inferential texts.
How to Ask Good Questions? Try to Leverage Paraphrases
Authors: Wenjie Zhou, Xu Sun, Yunfang Wu Xin Jia
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Given a sentence and its relevant answer, how to ask good questions is a challenging task, which has many real applications. Inspired by human`s paraphrasing capability to ask questions of the same meaning but with diverse expressions, we propose to incorporate paraphrase knowledge into question generation(QG) to generate human-like questions. Specifically, we present a two-hand hybrid model leveraging a self-built paraphrase resource, which is automatically conducted by a simple back-translation method. On the one hand, we conduct multi-task learning with sentence-level paraphrase generation (PG) as an auxiliary task to supplement paraphrase knowledge to the task-share encoder. On the other hand, we adopt a new loss function for diversity training to introduce more question patterns to QG. Extensive experimental results show that our proposed model obtains obvious performance gain over several strong baselines, and further human evaluation validates that our model can ask questions of high quality by leveraging paraphrase knowledge.
NeuInfer: Knowledge Inference on N-ary Facts
Authors: Xiaolong Jin, Jiafeng Guo, Yuanzhuo Wang, Xueqi Cheng Saiping Guan
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Knowledge inference on knowledge graph has attracted extensive attention, which aims to find out connotative valid facts in knowledge graph and is very helpful for improving the performance of many downstream applications. However, researchers have mainly poured attention to knowledge inference on binary facts. The studies on n-ary facts are relatively scarcer, although they are also ubiquitous in the real world. Therefore, this paper addresses knowledge inference on n-ary facts. We represent each n-ary fact as a primary triple coupled with a set of its auxiliary descriptive attribute-value pair(s). We further propose a neural network model, NeuInfer, for knowledge inference on n-ary facts. Besides handling the common task to infer an unknown element in a whole fact, NeuInfer can cope with a new type of task, flexible knowledge inference. It aims to infer an unknown element in a partial fact consisting of the primary triple coupled with any number of its auxiliary description(s). Experimental results demonstrate the remarkable superiority of NeuInfer.
Neural Graph Matching Networks for Chinese Short Text Matching
Authors: Yanbin Zhao, Boer Lyu, Lesheng Jin, Zhi Chen, Su Zhu, Kai Yu Lu Chen
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Chinese short text matching usually employs word sequences rather than character sequences to get better performance. However, Chinese word segmentation can be erroneous, ambiguous or inconsistent, which consequently hurts the final matching performance. To address this problem, we propose neural graph matching networks, a novel sentence matching framework capable of dealing with multi-granular input information. Instead of a character sequence or a single word sequence, paired word lattices formed from multiple word segmentation hypotheses are used as input and the model learns a graph representation according to an attentive graph matching mechanism. Experiments on two Chinese datasets show that our models outperform the state-of-the-art short text matching models.
Neural Mixed Counting Models for Dispersed Topic Discovery
Authors: Yanghui Rao, Zusheng Zhang, Haoran Xie, Qing Li, Fu Lee Wang, Ziye Chen Jiemin Wu
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Mixed counting models that use the negative binomial distribution as the prior can well model over-dispersed and hierarchically dependent random variables; thus they have attracted much attention in mining dispersed document topics. However, the existing parameter inference method like Monte Carlo sampling is quite time-consuming. In this paper, we propose two efficient neural mixed counting models, i.e., the Negative Binomial-Neural Topic Model (NB-NTM) and the Gamma Negative Binomial-Neural Topic Model (GNB-NTM) for dispersed topic discovery. Neural variational inference algorithms are developed to infer model parameters by using the reparameterization of Gamma distribution and the Gaussian approximation of Poisson distribution. Experiments on real-world datasets indicate that our models outperform state-of-the-art baseline models in terms of perplexity and topic coherence. The results also validate that both NB-NTM and GNB-NTM can produce explainable intermediate variables by generating dispersed proportions of document topics.
Reasoning Over Semantic-Level Graph for Fact Checking
Authors: Jingjing Xu, Duyu Tang, Zenan Xu, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin Wanjun Zhong
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Fact checking is a challenging task because verifying the truthfulness of a claim requires reasoning about multiple retrievable evidence. In this work, we present a method suitable for reasoning about the semantic-level structure of evidence. Unlike most previous works, which typically represent evidence sentences with either string concatenation or fusing the features of isolated evidence sentences, our approach operates on rich semantic structures of evidence obtained by semantic role labeling. We propose two mechanisms to exploit the structure of evidence while leveraging the advances of pre-trained models like BERT, GPT or XLNet. Specifically, using XLNet as the backbone, we first utilize the graph structure to re-define the relative distances of words, with the intuition that semantically related words should have short distances. Then, we adopt graph convolutional network and graph attention network to propagate and aggregate information from neighboring nodes on the graph. We evaluate our system on FEVER, a benchmark dataset for fact checking, and find that rich structural information is helpful and both our graph-based mechanisms improve the accuracy. Our model is the state-of-the-art system in terms of both official evaluation metrics, namely claim verification accuracy and FEVER score.
Automatic Generation of Citation Texts in Scholarly Papers: A Pilot Study
Authors: Xiaosheng Fan, Xiaojun Wan Xinyu Xing
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: In this paper, we study the challenging problem of automatic generation of citation texts in scholarly papers. Given the context of a citing paper A and a cited paper B, the task aims to generate a short text to describe B in the given context of A. One big challenge for addressing this task is the lack of training data. Usually, explicit citation texts are easy to extract, but it is not easy to extract implicit citation texts from scholarly papers. We thus first train an implicit citation extraction model based on BERT and leverage the model to construct a large training dataset for the citation text generation task. Then we propose and train a multi-source pointer-generator network with cross attention mechanism for citation text generation. Empirical evaluation results on a manually labeled test dataset verify the efficacy of our model. This pilot study confirms the feasibility of automatically generating citation texts in scholarly papers and the technique has the great potential to help researchers prepare their scientific papers.
Composing Elementary Discourse Units in Abstractive Summarization
Authors: Wenhao Wu, Sujian Li Zhenwen Li
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: In this paper, we argue that elementary discourse unit (EDU) is a more appropriate textual unit of content selection than the sentence unit in abstractive summarization. To well handle the problem of composing EDUs into an informative and fluent summary, we propose a novel summarization method that first designs an EDU selection model to extract and group informative EDUs and then an EDU fusion model to fuse the EDUs in each group into one sentence. We also design the reinforcement learning mechanism to use EDU fusion results to reward the EDU selection action, boosting the final summarization performance. Experiments on CNN/Daily Mail have demonstrated the effectiveness of our model.
Extractive Summarization as Text Matching
Authors: Pengfei Liu, Yiran Chen, Danqing Wang, Xipeng Qiu, Xuanjing Huang Ming Zhong
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems. Instead of following the commonly used framework of extracting sentences individually and modeling the relationship between sentences, we formulate the extractive summarization task as a semantic text matching problem, in which a source document and candidate summaries will be (extracted from the original text) matched in a semantic space. Notably, this paradigm shift to semantic matching framework is well-grounded in our comprehensive analysis of the inherent gap between sentence-level and summary-level extractors based on the property of the dataset. Besides, even instantiating the framework with a simple form of a matching model, we have driven the state-of-the-art extractive result on CNN/DailyMail to a new level (44.41 in ROUGE-1). Experiments on the other five datasets also show the effectiveness of the matching framework. We believe the power of this matching-based summarization framework has not been fully exploited. To encourage more instantiations in the future, we have released our codes, processed dataset, as well as generated summaries in r̆lhttps://github.com/maszhongming/MatchSum.
Heterogeneous Graph Neural Networks for Extractive Document Summarization
Authors: Pengfei Liu, Yining Zheng, Xipeng Qiu, Xuanjing Huang Danqing Wang
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: As a crucial step in extractive document summarization, learning cross-sentence relations has been explored by a plethora of approaches. An intuitive way is to put them in the graph-based neural network, which has a more complex structure for capturing inter-sentence relationships. In this paper, we present a heterogeneous graph-based neural network for extractive summarization (HETERSUMGRAPH), which contains semantic nodes of different granularity levels apart from sentences. These additional nodes act as the intermediary between sentences and enrich the cross-sentence relations. Besides, our graph structure is flexible in natural extension from a single-document setting to multi-document via introducing document nodes. To our knowledge, we are the first one to introduce different types of nodes into graph-based neural networks for extractive document summarization and perform a comprehensive qualitative analysis to investigate their benefits. The code will be released on Github.
Jointly Learning to Align and Summarize for Neural Cross-Lingual Summarization
Authors: Hui Liu, Xiaojun Wan Yue Cao
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Cross-lingual summarization is the task of generating a summary in one language given a text in a different language. Previous works on cross-lingual summarization mainly focus on using pipeline methods or training an end-to-end model using the translated parallel data. However, it is a big challenge for the model to directly learn cross-lingual summarization as it requires learning to understand different languages and learning how to summarize at the same time. In this paper, we propose to ease the cross-lingual summarization training by jointly learning to align and summarize. We design relevant loss functions to train this framework and propose several methods to enhance the isomorphism and cross-lingual transfer between languages. Experimental results show that our model can outperform competitive models in most cases. In addition, we show that our model even has the ability to generate cross-lingual summaries without access to any cross-lingual corpus.
Leveraging Graph to Improve Abstractive Multi-Document Summarization
Authors: Xinyan Xiao, Jiachen Liu, Hua Wu, Haifeng Wang, Junping Du Wei Li
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Graphs that capture relations between textual units have great benefits for detecting salient information from multiple documents and generating overall coherent summaries. In this paper, we develop a neural abstractive multi-document summarization (MDS) model which can leverage well-known graph representations of documents such as similarity graph and discourse graph, to more effectively process multiple input documents and produce abstractive summaries. Our model utilizes graphs to encode documents in order to capture cross-document relations, which is crucial to summarizing long documents. Our model can also take advantage of graphs to guide the summary generation process, which is beneficial for generating coherent and concise summaries. Furthermore, pre-trained language models can be easily combined with our model, which further improve the summarization performance significantly. Empirical results on the WikiSum and MultiNews dataset show that the proposed architecture brings substantial improvements over several strong baselines.
Multi-Granularity Interaction Network for Extractive and Abstractive Multi-Document Summarization
Authors: Tianming Wang, Xiaojun Wan Hanqi Jin
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: In this paper, we propose a multi-granularity interaction network for extractive and abstractive multi-document summarization, which jointly learn semantic representations for words, sentences, and documents. The word representations are used to generate an abstractive summary while the sentence representations are used to produce an extractive summary. We employ attention mechanisms to interact between different granularity of semantic representations, which helps to capture multi-granularity key information and improves the performance of both abstractive and extractive summarization. Experiment results show that our proposed model substantially outperforms all strong baseline methods and achieves the best results on the Multi-News dataset.
Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference
Authors: Dan Klein Nikita Kitaev
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: We present a constituency parsing algorithm that, like a supertagger, works by assigning labels to each word in a sentence. In order to maximally leverage current neural architectures, the model scores each word`s tags in parallel, with minimal task-specific structure. After scoring, a left-to-right reconciliation phase extracts a tree in (empirically) linear time. Our parser achieves 95.4 F1 on the WSJ test set while also achieving substantial speedups compared to current state-of-the-art parsers with comparable accuracies.
Are we Estimating or Guesstimating Translation Quality?
Authors: Francisco Guzmán, Lucia Specia Shuo Sun
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Recent advances in pre-trained multilingual language models lead to state-of-the-art results on the task of quality estimation (QE) for machine translation. A carefully engineered ensemble of such models won the QE shared task at WMT19. Our in-depth analysis, however, shows that the success of using pre-trained language models for QE is over-estimated due to three issues we observed in current QE datasets: (i) The distributions of quality scores are imbalanced and skewed towards good quality scores; (iii) QE models can perform well on these datasets while looking at only source or translated sentences; (iii) They contain statistical artifacts that correlate well with human-annotated QE labels. Our findings suggest that although QE models might capture fluency of translated sentences and complexity of source sentences, they cannot model adequacy of translations effectively.
Language (Re)modelling: Towards Embodied Language Understanding
Authors: Chen Shani, Tom Hope, Miriam R L Petruck, Omri Abend, Dafna Shahaf Ronen Tamari
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: While natural language understanding (NLU) is advancing rapidly, today`s technology differs from human-like language understanding in fundamental ways, notably in its inferior efficiency, interpretability, and generalization. This work proposes an approach to representation and learning based on the tenets of embodied cognitive linguistics (ECL). According to ECL, natural language is inherently executable (like programming languages), driven by mental simulation and metaphoric mappings over hierarchical compositions of structures and schemata learned through embodied interaction. This position paper argues that the use of grounding by metaphoric reasoning and simulation will greatly benefit NLU systems, and proposes a system architecture along with a roadmap towards realizing this vision.
The State and Fate of Linguistic Diversity and Inclusion in the NLP World
Authors: Sebastin Santy, Amar Budhiraja, Kalika Bali, Monojit Choudhury Pratik Joshi
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Language technologies contribute to promoting multilingualism and linguistic diversity around the world. However, only a very small number of the over 7000 languages of the world are represented in the rapidly evolving language technologies and applications. In this paper we look at the relation between the types of languages, resources, and their representation in NLP conferences to understand the trajectory that different languages have followed over time. Our quantitative investigation underlines the disparity between languages, especially in terms of their resources, and calls into question the “language agnostic” status of current models and systems. Through this paper, we attempt to convince the ACL community to prioritise the resolution of the predicaments highlighted here, so that no language is left behind.
The Unstoppable Rise of Computational Linguistics in Deep Learning
Authors: James Henderson
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: In this paper, we trace the history of neural networks applied to natural language understanding tasks, and identify key contributions which the nature of language has made to the development of neural network architectures. We focus on the importance of variable binding and its instantiation in attention-based models, and argue that Transformer is not a sequence model but an induced-structure model. This perspective leads to predictions of the challenges facing research in deep learning architectures for natural language understanding.
To Boldly Query What No One Has Annotated Before? The Frontiers of Corpus Querying
Authors: Kerstin Jung Markus Gärtner
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Corpus query systems exist to address the multifarious information needs of any person interested in the content of annotated corpora. In this role they play an important part in making those resources usable for a wider audience. Over the past decades, several such query systems and languages have emerged, varying greatly in their expressiveness and technical details. This paper offers a broad overview of the history of corpora and corpus query tools. It focusses strongly on the query side and hints at exciting directions for future development.
A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking
Authors: Zekang Li, Jinchao Zhang, Fandong Meng, Yang Feng, Cheng Niu, Jie Zhou Yong Shan
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Recent studies in dialogue state tracking (DST) leverage historical information to determine states which are generally represented as slot-value pairs. However, most of them have limitations to efficiently exploit relevant context due to the lack of a powerful mechanism for modeling interactions between the slot and the dialogue history. Besides, existing methods usually ignore the slot imbalance problem and treat all slots indiscriminately, which limits the learning of hard slots and eventually hurts overall performance. In this paper, we propose to enhance the DST through employing a contextual hierarchical attention network to not only discern relevant information at both word level and turn level but also learn contextual representations. We further propose an adaptive objective to alleviate the slot imbalance problem by dynamically adjust weights of different slots during training. Experimental results show that our approach reaches 52.68% and 58.55% joint accuracy on MultiWOZ 2.0 and MultiWOZ 2.1 datasets respectively and achieves new state-of-the-art performance with considerable improvements (+1.24% and +5.98%).
Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight
Authors: Hongshen Chen, Yonghao Song, Cheng Zhang, Xiaofang Zhao, Dawei Yin Hengyi Cai
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Current state-of-the-art neural dialogue models learn from human conversations following the data-driven paradigm. As such, a reliable training corpus is the crux of building a robust and well-behaved dialogue model. However, due to the open-ended nature of human conversations, the quality of user-generated training data varies greatly, and effective training samples are typically insufficient while noisy samples frequently appear. This impedes the learning of those data-driven neural dialogue models. Therefore, effective dialogue learning requires not only more reliable learning samples, but also fewer noisy samples. In this paper, we propose a data manipulation framework to proactively reshape the data distribution towards reliable samples by augmenting and highlighting effective learning samples as well as reducing the effect of inefficient samples simultaneously. In particular, the data manipulation model selectively augments the training samples and assigns an importance weight to each instance to reform the training data. Note that, the proposed data manipulation framework is fully data-driven and learnable. It not only manipulates training samples to optimize the dialogue generation model, but also learns to increase its manipulation skills through gradient descent with validation samples. Extensive experiments show that our framework can improve the dialogue generation performance with respect to various automatic evaluation metrics and human judgments.
Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog
Authors: Xiao Xu, Wanxiang Che, Yue Zhang, Ting Liu Libo Qin
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Recent studies have shown remarkable success in end-to-end task-oriented dialog system. However, most neural models rely on large training data, which are only available for a certain number of task domains, such as navigation and scheduling. This makes it difficult to scalable for a new domain with limited labeled data. However, there has been relatively little research on how to effectively use data from all domains to improve the performance of each domain and also unseen domains. To this end, we investigate methods that can make explicit use of domain knowledge and introduce a shared-private network to learn shared and specific knowledge. In addition, we propose a novel Dynamic Fusion Network (DF-Net) which automatically exploit the relevance between the target domain and each domain. Results show that our models outperforms existing methods on multi-domain dialogue, giving the state-of-the-art in the literature. Besides, with little training data, we show its transferability by outperforming prior best model by 13.9% on average.
Learning Efficient Dialogue Policy from Demonstrations through Shaping
Authors: Baolin Peng, Kam-Fai Wong Huimin Wang
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Training a task-oriented dialogue agent with reinforcement learning is prohibitively expensive since it requires a large volume of interactions with users. Human demonstrations can be used to accelerate learning progress. However, how to effectively leverage demonstrations to learn dialogue policy remains less explored. In this paper, we present S\textasciicircum2Agent that efficiently learns dialogue policy from demonstrations through policy shaping and reward shaping. We use an imitation model to distill knowledge from demonstrations, based on which policy shaping estimates feedback on how the agent should act in policy space. Reward shaping is then incorporated to bonus state-actions similar to demonstrations explicitly in value space encouraging better exploration. The effectiveness of the proposed S\textasciicircum2Agentt is demonstrated in three dialogue domains and a challenging domain adaptation task with both user simulator evaluation and human evaluation.
SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing
Authors: Yan Yang, Chencai Chen, Liang He, Zhou Yu Jiaying Hu
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Dialogue state tracker is responsible for inferring user intentions through dialogue history. Previous methods have difficulties in handling dialogues with long interaction context, due to the excessive information. We propose a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce redundant information`s interference and improve long dialogue context tracking. Specially, we first apply a Slot Attention to learn a set of slot-specific features from the original dialogue and then integrate them using a slot information sharing module. Our model yields a significantly improved performance compared to previous state-of the-art models on the MultiWOZ dataset.
Speaker Sensitive Response Evaluation Model
Authors: Alice Oh JinYeong Bak
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Automatic evaluation of open-domain dialogue response generation is very challenging because there are many appropriate responses for a given context. Existing evaluation models merely compare the generated response with the ground truth response and rate many of the appropriate responses as inappropriate if they deviate from the ground truth. One approach to resolve this problem is to consider the similarity of the generated response with the conversational context. In this paper, we propose an automatic evaluation model based on that idea and learn the model parameters from an unlabeled conversation corpus. Our approach considers the speakers in defining the different levels of similar context. We use a Twitter conversation corpus that contains many speakers and conversations to test our evaluation model. Experiments show that our model outperforms the other existing evaluation metrics in terms of high correlation with human annotation scores. We also show that our model trained on Twitter can be applied to movie dialogues without any additional training. We provide our code and the learned parameters so that they can be used for automatic evaluation of dialogue response generation models.
A Top-down Neural Architecture towards Text-level Parsing of Discourse Rhetorical Structure
Authors: Yuqing Xing, Fang Kong, Peifeng Li, Guodong Zhou Longyin Zhang
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Due to its great importance in deep natural language understanding and various down-stream applications, text-level parsing of discourse rhetorical structure (DRS) has been drawing more and more attention in recent years. However, all the previous studies on text-level discourse parsing adopt bottom-up approaches, which much limit the DRS determination on local information and fail to well benefit from global information of the overall discourse. In this paper, we justify from both computational and perceptive points-of-view that the top-down architecture is more suitable for text-level DRS parsing. On the basis, we propose a top-down neural architecture toward text-level DRS parsing. In particular, we cast discourse parsing as a recursive split point ranking task, where a split point is classified to different levels according to its rank and the elementary discourse units (EDUs) associated with it are arranged accordingly. In this way, we can determine the complete DRS as a hierarchical tree structure via an encoder-decoder with an internal stack. Experimentation on both the English RST-DT corpus and the Chinese CDTB corpus shows the great effectiveness of our proposed top-down approach towards text-level DRS parsing.
Amalgamation of protein sequence, structure and textual information for improving protein-protein interaction identification
Authors: Sriparna Saha Pratik Dutta
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: An in-depth exploration of protein-protein interactions (PPI) is essential to understand the metabolism in addition to the regulations of biological entities like proteins, carbohydrates, and many more. Most of the recent PPI tasks in BioNLP domain have been carried out solely using textual data. In this paper, we argue that incorporating multimodal cues can improve the automatic identification of PPI. As a first step towards enabling the development of multimodal approaches for PPI identification, we have developed two multi-modal datasets which are extensions and multi-modal versions of two popular benchmark PPI corpora (BioInfer and HRPD50). Besides, existing textual modalities, two new modalities, 3D protein structure and underlying genomic sequence, are also added to each instance. Further, a novel deep multi-modal architecture is also implemented to efficiently predict the protein interactions from the developed datasets. A detailed experimental analysis reveals the superiority of the multi-modal approach in comparison to the strong baselines including unimodal approaches and state-of the-art methods over both the generated multi-modal datasets. The developed multi-modal datasets are available for use at r̆lhttps://github.com/sduttap16/MM_PPI_NLP.
Bipartite Flat-Graph Network for Nested Named Entity Recognition
Authors: Hai Zhao Ying Luo
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: In this paper, we propose a novel bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER), which contains two subgraph modules: a flat NER module for outermost entities and a graph module for all the entities located in inner layers. Bidirectional LSTM (BiLSTM) and graph convolutional network (GCN) are adopted to jointly learn flat entities and their inner dependencies. Different from previous models, which only consider the unidirectional delivery of information from innermost layers to outer ones (or outside-to-inside), our model effectively captures the bidirectional interaction between them. We first use the entities recognized by the flat NER module to construct an entity graph, which is fed to the next graph module. The richer representation learned from graph module carries the dependencies of inner entities and can be exploited to improve outermost entity predictions. Experimental results on three standard nested NER datasets demonstrate that our BiFlaG outperforms previous state-of-the-art models.
Connecting Embeddings for Knowledge Graph Entity Typing
Authors: Anxiang Zhang, Ruobing Xie, Kang Liu, Xiaojie Wang Yu Zhao
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Knowledge graph (KG) entity typing aims at inferring possible missing entity type instances in KG, which is a very significant but still under-explored subtask of knowledge graph completion. In this paper, we propose a novel approach for KG entity typing which is trained by jointly utilizing local typing knowledge from existing entity type assertions and global triple knowledge in KGs. Specifically, we present two distinct knowledge-driven effective mechanisms of entity type inference. Accordingly, we build two novel embedding models to realize the mechanisms. Afterward, a joint model via connecting them is used to infer missing entity type instances, which favors inferences that agree with both entity type instances and triple knowledge in KGs. Experimental results on two real-world datasets (Freebase and YAGO) demonstrate the effectiveness of our proposed mechanisms and models for improving KG entity typing. The source code and data of this paper can be obtained from: r̆lhttps://github.com/Adam1679/ConnectE .
Continual Relation Learning via Episodic Memory Activation and Reconsolidation
Authors: Yi Dai, Tianyu Gao, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou Xu Han
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Continual relation learning aims to continually train a model on new data to learn incessantly emerging novel relations while avoiding catastrophically forgetting old relations. Some pioneering work has proved that storing a handful of historical relation examples in episodic memory and replaying them in subsequent training is an effective solution for such a challenging problem. However, these memory-based methods usually suffer from overfitting the few memorized examples of old relations, which may gradually cause inevitable confusion among existing relations. Inspired by the mechanism in human long-term memory formation, we introduce episodic memory activation and reconsolidation (EMAR) to continual relation learning. Every time neural models are activated to learn both new and memorized data, EMAR utilizes relation prototypes for memory reconsolidation exercise to keep a stable understanding of old relations. The experimental results show that EMAR could get rid of catastrophically forgetting old relations and outperform the state-of-the-art continual learning models.
Handling Rare Entities for Neural Sequence Labeling
Authors: Han Li, Kaisheng Yao, Xiaolong Li Yangming Li
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: One great challenge in neural sequence labeling is the data sparsity problem for rare entity words and phrases. Most of test set entities appear only few times and are even unseen in training corpus, yielding large number of out-of-vocabulary (OOV) and low-frequency (LF) entities during evaluation. In this work, we propose approaches to address this problem. For OOV entities, we introduce local context reconstruction to implicitly incorporate contextual information into their representations. For LF entities, we present delexicalized entity identification to explicitly extract their frequency-agnostic and entity-type-specific representations. Extensive experiments on multiple benchmark datasets show that our model has significantly outperformed all previous methods and achieved new start-of-the-art results. Notably, our methods surpass the model fine-tuned on pre-trained language models without external resource.
Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition
Authors: Jun Suzuki, Sosuke Kobayashi, Sho Yokoi, Tatsuki Kuribayashi, Ryuto Konno, Kentaro Inui Hiroki Ouchi
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Interpretable rationales for model predictions play a critical role in practical applications. In this study, we develop models possessing interpretable inference process for structured prediction. Specifically, we present a method of instance-based learning that learns similarities between spans. At inference time, each span is assigned a class label based on its similar spans in the training set, where it is easy to understand how much each training instance contributes to the predictions. Through empirical analysis on named entity recognition, we demonstrate that our method enables to build models that have high interpretability without sacrificing performance.
MIE: A Medical Information Extractor towards Medical Dialogues
Authors: Zhongtao Jiang, Tao Zhang, Shiwan Liu, Jiarun Cao, Kang Liu, Shengping Liu, Jun Zhao Yuanzhe Zhang
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Electronic Medical Records (EMRs) have become key components of modern medical care systems. Despite the merits of EMRs, many doctors suffer from writing them, which is time-consuming and tedious. We believe that automatically converting medical dialogues to EMRs can greatly reduce the burdens of doctors, and extracting information from medical dialogues is an essential step. To this end, we annotate online medical consultation dialogues in a window-sliding style, which is much easier than the sequential labeling annotation. We then propose a Medical Information Extractor (MIE) towards medical dialogues. MIE is able to extract mentioned symptoms, surgeries, tests, other information and their corresponding status. To tackle the particular challenges of the task, MIE uses a deep matching architecture, taking dialogue turn-interaction into account. The experimental results demonstrate MIE is a promising solution to extract medical information from doctor-patient dialogues.
Named Entity Recognition as Dependency Parsing
Authors: Bernd Bohnet, Massimo Poesio Juntao Yu
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Named Entity Recognition (NER) is a fundamental task in Natural Language Processing, concerned with identifying spans of text expressing references to entities. NER research is often focused on flat entities only (flat NER), ignoring the fact that entity references can be nested, as in [Bank of [China]] (Finkel and Manning, 2009). In this paper, we use ideas from graph-based dependency parsing to provide our model a global view on the input via a biaffine model (Dozat and Manning, 2017). The biaffine model scores pairs of start and end tokens in a sentence which we use to explore all spans, so that the model is able to predict named entities accurately. We show that the model works well for both nested and flat NER through evaluation on 8 corpora and achieving SoTA performance on all of them, with accuracy gains of up to 2.2 percentage points.