mtybilly/Instruct2DS huggingface dataset:
Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment
Authors: Yinpei Dai, Hangyu Li, Chengguang Tang, Yongbin Li, Jian Sun, Xiaodan Zhu
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Existing end-to-end dialog systems perform less effectively when data is scarce. To obtain an acceptable success in real-life online services with only a handful of training examples, both fast adaptability and reliable performance are highly desirable for dialog systems. In this paper, we propose the Meta-Dialog System (MDS), which combines the advantages of both meta-learning approaches and human-machine collaboration. We evaluate our methods on a new extended-bAbI dataset and a transformed MultiWOZ dataset for low-resource goal-oriented dialog learning. Experimental results show that MDS significantly outperforms non-meta-learning baselines and can achieve more than 90% per-turn accuracies with only 10 dialogs on the extended-bAbI dataset.
Learning to Tag OOV Tokens by Integrating Contextual Representation and Background Knowledge
Authors: Keqing He, Yuanmeng Yan, Weiran Xu
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Neural-based context-aware models for slot tagging have achieved state-of-the-art performance. However, the presence of OOV(out-of-vocab) words significantly degrades the performance of neural-based models, especially in a few-shot scenario. In this paper, we propose a novel knowledge-enhanced slot tagging model to integrate contextual representation of input text and the large-scale lexical background knowledge. Besides, we use multi-level graph attention to explicitly model lexical relations. The experiments show that our proposed knowledge integration mechanism achieves consistent improvements across settings with different sizes of training data on two public benchmark datasets.
Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition
Authors: Ryuichi Takanobu, Runze Liang, Minlie Huang
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Many studies have applied reinforcement learning to train a dialog policy and show great promise these years. One common approach is to employ a user simulator to obtain a large number of simulated user experiences for reinforcement learning algorithms. However, modeling a realistic user simulator is challenging. A rule-based simulator requires heavy domain expertise for complex tasks, and a data-driven simulator requires considerable data and it is even unclear how to evaluate a simulator. To avoid explicitly building a user simulator beforehand, we propose Multi-Agent Dialog Policy Learning, which regards both the system and the user as the dialog agents. Two agents interact with each other and are jointly learned simultaneously. The method uses the actor-critic framework to facilitate pretraining and improve scalability. We also propose Hybrid Value Network for the role-aware reward decomposition to integrate role-specific domain knowledge of each agent in the task-oriented dialog. Results show that our method can successfully build a system policy and a user policy simultaneously, and two agents can achieve a high task success rate through conversational interaction.
Paraphrase Augmented Task-Oriented Dialog Generation
Authors: Silin Gao, Yichi Zhang, Zhijian Ou, Zhou Yu
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Neural generative models have achieved promising performance on dialog generation tasks if given a huge data set. However, the lack of high-quality dialog data and the expensive data annotation process greatly limit their application in real world settings. We propose a paraphrase augmented response generation (PARG) framework that jointly trains a paraphrase model and a response generation model to improve the dialog generation performance. We also design a method to automatically construct paraphrase training data set based on dialog state and dialog act labels. PARG is applicable to various dialog generation models, such as TSCP (Lei et al., 2018) and DAMD (Zhang et al., 2019). Experimental results show that the proposed framework improves these state-of-the-art dialog models further on CamRest676 and MultiWOZ. PARG also outperforms other data augmentation methods significantly in dialog generation tasks, especially under low resource settings.
Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation
Authors: Zhiliang Tian, Wei Bi, Dongkyu Lee, Lanqing Xue, Yiping Song, Xiaojiang Liu, Nevin L. Zhang
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Neural conversation models are known to generate appropriate but non-informative responses in general. A scenario where informativeness can be significantly enhanced is Conversing by Reading (CbR), where conversations take place with respect to a given external document. In previous work, the external document is utilized by (1) creating a context-aware document memory that integrates information from the document and the conversational context, and then (2) generating responses referring to the memory. In this paper, we propose to create the document memory with some anticipated responses in mind. This is achieved using a teacher-student framework. The teacher is given the external document, the context, and the ground-truth response, and learns how to build a response-aware document memory from three sources of information. The student learns to construct a response-anticipated document memory from the first two sources, and teacher`s insight on memory creation. Empirical results show that our model outperforms the previous state-of-the-art for the CbR task.
Semi-Supervised Dialogue Policy Learning via Stochastic Reward Estimation
Authors: Xinting Huang, Jianzhong Qi, Yu Sun, Rui Zhang
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Dialogue policy optimization often obtains feedback until task completion in task-oriented dialogue systems. This is insufficient for training intermediate dialogue turns since supervision signals (or rewards) are only provided at the end of dialogues. To address this issue, reward learning has been introduced to learn from state-action pairs of an optimal policy to provide turn-by-turn rewards. This approach requires complete state-action annotations of human-to-human dialogues (i.e., expert demonstrations), which is labor intensive. To overcome this limitation, we propose a novel reward learning approach for semi-supervised policy learning. The proposed approach learns a dynamics model as the reward function which models dialogue progress (i.e., state-action sequences) based on expert demonstrations, either with or without annotations. The dynamics model computes rewards by predicting whether the dialogue progress is consistent with expert demonstrations. We further propose to learn action embeddings for a better generalization of the reward function. The proposed approach outperforms competitive policy learning baselines on MultiWOZ, a benchmark multi-domain dataset.
Towards Unsupervised Language Understanding and Generation by Joint Dual Learning
Authors: Shang-Yu Su, Chao-Wei Huang, Yun-Nung Chen
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: In modular dialogue systems, natural language understanding (NLU) and natural language generation (NLG) are two critical components, where NLU extracts the semantics from the given texts and NLG is to construct corresponding natural language sentences based on the input semantic representations. However, the dual property between understanding and generation has been rarely explored. The prior work is the first attempt that utilized the duality between NLU and NLG to improve the performance via a dual supervised learning framework. However, the prior work still learned both components in a supervised manner; instead, this paper introduces a general learning framework to effectively exploit such duality, providing flexibility of incorporating both supervised and unsupervised learning algorithms to train language understanding and generation models in a joint fashion. The benchmark experiments demonstrate that the proposed approach is capable of boosting the performance of both NLU and NLG. The source code is available at: r̆lhttps://github.com/MiuLab/DuaLUG.
USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation
Authors: Shikib Mehri, Maxine Eskenazi
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: The lack of meaningful automatic evaluation metrics for dialog has impeded open-domain dialog research. Standard language generation metrics have been shown to be ineffective for evaluating dialog models. To this end, this paper presents USR, an UnSupervised and Reference-free evaluation metric for dialog. USR is a reference-free metric that trains unsupervised models to measure several desirable qualities of dialog. USR is shown to strongly correlate with human judgment on both Topical-Chat (turn-level: 0.42, system-level: 1.0) and PersonaChat (turn-level: 0.48 and system-level: 1.0). USR additionally produces interpretable measures for several desirable properties of dialog.
Explicit Semantic Decomposition for Definition Generation
Authors: Jiahuan Li, Yu Bao, Shujian Huang, Xinyu Dai, Jiajun Chen
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Definition generation, which aims to automatically generate dictionary definitions for words, has recently been proposed to assist the construction of dictionaries and help people understand unfamiliar texts. However, previous works hardly consider explicitly modeling the “components” of definitions, leading to under-specific generation results. In this paper, we propose ESD, namely Explicit Semantic Decomposition for definition Generation, which explicitly decomposes the meaning of words into semantic components, and models them with discrete latent variables for definition generation. Experimental results show that achieves top results on WordNet and Oxford benchmarks, outperforming strong previous baselines.
Improved Natural Language Generation via Loss Truncation
Authors: Daniel Kang, Tatsunori B. Hashimoto
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Neural language models are usually trained to match the distributional properties of large-scale corpora by minimizing the log loss. While straightforward to optimize, this approach forces the model to reproduce all variations in the dataset, including noisy and invalid references (e.g., misannotations and hallucinated facts). Even a small fraction of noisy data can degrade the performance of log loss. As an alternative, prior work has shown that minimizing the distinguishability of generated samples is a principled and robust loss that can handle invalid references. However, distinguishability has not been used in practice due to challenges in optimization and estimation. We propose loss truncation: a simple and scalable procedure which adaptively removes high log loss examples as a way to optimize for distinguishability. Empirically, we demonstrate that loss truncation outperforms existing baselines on distinguishability on a summarization task. Furthermore, we show that samples generated by the loss truncation model have factual accuracy ratings that exceed those of baselines and match human references.
Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks
Authors: Yanbin Zhao, Lu Chen, Zhi Chen, Ruisheng Cao, Su Zhu, Kai Yu
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Efficient structure encoding for graphs with labeled edges is an important yet challenging point in many graph-based models. This work focuses on AMR-to-text generation -- A graph-to-sequence task aiming to recover natural language from Abstract Meaning Representations (AMR). Existing graph-to-sequence approaches generally utilize graph neural networks as their encoders, which have two limitations: 1) The message propagation process in AMR graphs is only guided by the first-order adjacency information. 2) The relationships between labeled edges are not fully considered. In this work, we propose a novel graph encoding framework which can effectively explore the edge relations. We also adopt graph attention networks with higher-order neighborhood information to encode the rich structure in AMR graphs. Experiment results show that our approach obtains new state-of-the-art performance on English AMR benchmark datasets. The ablation analyses also demonstrate that both edge relations and higher-order information are beneficial to graph-to-sequence modeling.
Rigid Formats Controlled Text Generation
Authors: Piji Li, Haisong Zhang, Xiaojiang Liu, Shuming Shi
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Neural text generation has made tremendous progress in various tasks. One common characteristic of most of the tasks is that the texts are not restricted to some rigid formats when generating. However, we may confront some special text paradigms such as Lyrics (assume the music score is given), Sonnet, SongCi (classical Chinese poetry of the Song dynasty), etc. The typical characteristics of these texts are in three folds: (1) They must comply fully with the rigid predefined formats. (2) They must obey some rhyming schemes. (3) Although they are restricted to some formats, the sentence integrity must be guaranteed. To the best of our knowledge, text generation based on the predefined rigid formats has not been well investigated. Therefore, we propose a simple and elegant framework named SongNet to tackle this problem. The backbone of the framework is a Transformer-based auto-regressive language model. Sets of symbols are tailor-designed to improve the modeling performance especially on format, rhyme, and sentence integrity. We improve the attention mechanism to impel the model to capture some future information on the format. A pre-training and fine-tuning framework is designed to further improve the generation quality. Extensive experiments conducted on two collected corpora demonstrate that our proposed framework generates significantly better results in terms of both automatic metrics and the human evaluation.
Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation
Authors: Kaustubh Dhole, Christopher D. Manning
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Question Generation (QG) is fundamentally a simple syntactic transformation; however, many aspects of semantics influence what questions are good to form. We implement this observation by developing Syn-QG, a set of transparent syntactic rules leveraging universal dependencies, shallow semantic parsing, lexical resources, and custom rules which transform declarative sentences into question-answer pairs. We utilize PropBank argument descriptions and VerbNet state predicates to incorporate shallow semantic content, which helps generate questions of a descriptive nature and produce inferential and semantically richer questions than existing systems. In order to improve syntactic fluency and eliminate grammatically incorrect questions, we employ back-translation over the output of these syntactic rules. A set of crowd-sourced evaluations shows that our system can generate a larger number of highly grammatical and relevant questions than previous QG systems and that back-translation drastically improves grammaticality at a slight cost of generating irrelevant questions.
An Online Semantic-enhanced Dirichlet Model for Short Text Stream Clustering
Authors: Jay Kumar, Junming Shao, Salah Uddin, Wazir Ali
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Clustering short text streams is a challenging task due to its unique properties: infinite length, sparse data representation and cluster evolution. Existing approaches often exploit short text streams in a batch way. However, determine the optimal batch size is usually a difficult task since we have no priori knowledge when the topics evolve. In addition, traditional independent word representation in graphical model tends to cause “term ambiguity” problem in short text clustering. Therefore, in this paper, we propose an Online Semantic-enhanced Dirichlet Model for short sext stream clustering, called OSDM, which integrates the word-occurance semantic information (i.e., context) into a new graphical model and clusters each arriving short text automatically in an online way. Extensive results have demonstrated that OSDM has better performance compared to many state-of-the-art algorithms on both synthetic and real-world data sets.
Generative Semantic Hashing Enhanced via Boltzmann Machines
Authors: Lin Zheng, Qinliang Su, Dinghan Shen, Changyou Chen
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Generative semantic hashing is a promising technique for large-scale information retrieval thanks to its fast retrieval speed and small memory footprint. For the tractability of training, existing generative-hashing methods mostly assume a factorized form for the posterior distribution, enforcing independence among the bits of hash codes. From the perspectives of both model representation and code space size, independence is always not the best assumption. In this paper, to introduce correlations among the bits of hash codes, we propose to employ the distribution of Boltzmann machine as the variational posterior. To address the intractability issue of training, we first develop an approximate method to reparameterize the distribution of a Boltzmann machine by augmenting it as a hierarchical concatenation of a Gaussian-like distribution and a Bernoulli distribution. Based on that, an asymptotically-exact lower bound is further derived for the evidence lower bound (ELBO). With these novel techniques, the entire model can be optimized efficiently. Extensive experimental results demonstrate that by effectively modeling correlations among different bits within a hash code, our model can achieve significant performance gains.
Interactive Construction of User-Centric Dictionary for Text Analytics
Authors: Ryosuke Kohita, Issei Yoshida, Hiroshi Kanayama, Tetsuya Nasukawa
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: We propose a methodology to construct a term dictionary for text analytics through an interactive process between a human and a machine, which helps the creation of flexible dictionaries with precise granularity required in typical text analysis. This paper introduces the first formulation of interactive dictionary construction to address this issue. To optimize the interaction, we propose a new algorithm that effectively captures an analyst`s intention starting from only a small number of sample terms. Along with the algorithm, we also design an automatic evaluation framework that provides a systematic assessment of any interactive method for the dictionary creation task. Experiments using real scenario based corpora and dictionaries show that our algorithm outperforms baseline methods, and works even with a small number of interactions.
Tree-Structured Neural Topic Model
Authors: Masaru Isonuma, Junichiro Mori, Danushka Bollegala, Ichiro Sakata
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: This paper presents a tree-structured neural topic model, which has a topic distribution over a tree with an infinite number of branches. Our model parameterizes an unbounded ancestral and fraternal topic distribution by applying doubly-recurrent neural networks. With the help of autoencoding variational Bayes, our model improves data scalability and achieves competitive performance when inducing latent topics and tree structures, as compared to a prior tree-structured topic model (Blei et al., 2010). This work extends the tree-structured topic model such that it can be incorporated with neural models for downstream tasks.
Unsupervised FAQ Retrieval with Question Generation and BERT
Authors: Yosi Mass, Boaz Carmeli, Haggai Roitman, David Konopnicki
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: We focus on the task of Frequently Asked Questions (FAQ) retrieval. A given user query can be matched against the questions and/or the answers in the FAQ. We present a fully unsupervised method that exploits the FAQ pairs to train two BERT models. The two models match user queries to FAQ answers and questions, respectively. We alleviate the missing labeled data of the latter by automatically generating high-quality question paraphrases. We show that our model is on par and even outperforms supervised models on existing datasets.
“The Boating Store Had Its Best Sail Ever”: Pronunciation-attentive Contextualized Pun Recognition
Authors: Yichao Zhou, Jyun-Yu Jiang, Jieyu Zhao, Kai-Wei Chang, Wei Wang
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Humor plays an important role in human languages and it is essential to model humor when building intelligence systems. Among different forms of humor, puns perform wordplay for humorous effects by employing words with double entendre and high phonetic similarity. However, identifying and modeling puns are challenging as puns usually involved implicit semantic or phonological tricks. In this paper, we propose Pronunciation-attentive Contextualized Pun Recognition (PCPR) to perceive human humor, detect if a sentence contains puns and locate them in the sentence. PCPR derives contextualized representation for each word in a sentence by capturing the association between the surrounding context and its corresponding phonetic symbols. Extensive experiments are conducted on two benchmark datasets. Results demonstrate that the proposed approach significantly outperforms the state-of-the-art methods in pun detection and location tasks. In-depth analyses verify the effectiveness and robustness of PCPR.
Fast and Accurate Deep Bidirectional Language Representations for Unsupervised Learning
Authors: Joongbo Shin, Yoonhyung Lee, Seunghyun Yoon, Kyomin Jung
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Even though BERT has achieved successful performance improvements in various supervised learning tasks, BERT is still limited by repetitive inferences on unsupervised tasks for the computation of contextual language representations. To resolve this limitation, we propose a novel deep bidirectional language model called a Transformer-based Text Autoencoder (T-TA). The T-TA computes contextual language representations without repetition and displays the benefits of a deep bidirectional architecture, such as that of BERT. In computation time experiments in a CPU environment, the proposed T-TA performs over six times faster than the BERT-like model on a reranking task and twelve times faster on a semantic similarity task. Furthermore, the T-TA shows competitive or even better accuracies than those of BERT on the above tasks. Code is available at r̆lhttps://github.com/joongbo/tta.
Fine-grained Interest Matching for Neural News Recommendation
Authors: Heyuan Wang, Fangzhao Wu, Zheng Liu, Xing Xie
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Personalized news recommendation is a critical technology to improve users' online news reading experience. The core of news recommendation is accurate matching between user`s interests and candidate news. The same user usually has diverse interests that are reflected in different news she has browsed. Meanwhile, important semantic features of news are implied in text segments of different granularities. Existing studies generally represent each user as a single vector and then match the candidate news vector, which may lose fine-grained information for recommendation. In this paper, we propose FIM, a Fine-grained Interest Matching method for neural news recommendation. Instead of aggregating user`s all historical browsed news into a unified vector, we hierarchically construct multi-level representations for each news via stacked dilated convolutions. Then we perform fine-grained matching between segment pairs of each browsed news and the candidate news at each semantic level. High-order salient signals are then identified by resembling the hierarchy of image recognition for final click prediction. Extensive experiments on a real-world dataset from MSN news validate the effectiveness of our model on news recommendation.
Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder
Authors: Fan Zhou, Shengming Zhang, Yi Yang
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Operational risk management is one of the biggest challenges nowadays faced by financial institutions. There are several major challenges of building a text classification system for automatic operational risk prediction, including imbalanced labeled/unlabeled data and lacking interpretability. To tackle these challenges, we present a semi-supervised text classification framework that integrates multi-head attention mechanism with Semi-supervised variational inference for Operational Risk Classification (SemiORC). We empirically evaluate the framework on a real-world dataset. The results demonstrate that our method can better utilize unlabeled data and learn visually interpretable document representations. SemiORC also outperforms other baseline methods on operational risk classification.
Interpreting Twitter User Geolocation
Authors: Ting Zhong, Tianliang Wang, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, Yi Yang
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Identifying user geolocation in online social networks is an essential task in many location-based applications. Existing methods rely on the similarity of text and network structure, however, they suffer from a lack of interpretability on the corresponding results, which is crucial for understanding model behavior. In this work, we adopt influence functions to interpret the behavior of GNN-based models by identifying the importance of training users when predicting the locations of the testing users. This methodology helps with providing meaningful explanations on prediction results. Furthermore, it also initiates an attempt to uncover the so-called “black-box” GNN-based models by investigating the effect of individual nodes.
Modeling Code-Switch Languages Using Bilingual Parallel Corpus
Authors: Grandee Lee, Haizhou Li
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Language modeling is the technique to estimate the probability of a sequence of words. A bilingual language model is expected to model the sequential dependency for words across languages, which is difficult due to the inherent lack of suitable training data as well as diverse syntactic structure across languages. We propose a bilingual attention language model (BALM) that simultaneously performs language modeling objective with a quasi-translation objective to model both the monolingual as well as the cross-lingual sequential dependency. The attention mechanism learns the bilingual context from a parallel corpus. BALM achieves state-of-the-art performance on the SEAME code-switch database by reducing the perplexity of 20.5% over the best-reported result. We also apply BALM in bilingual lexicon induction, and language normalization tasks to validate the idea.
SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
Authors: Xingyi Cheng, Weidi Xu, Kunlong Chen, Shaohua Jiang, Feng Wang, Taifeng Wang, Wei Chu, Yuan Qi
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Chinese Spelling Check (CSC) is a task to detect and correct spelling errors in Chinese natural language. Existing methods have made attempts to incorporate the similarity knowledge between Chinese characters. However, they take the similarity knowledge as either an external input resource or just heuristic rules. This paper proposes to incorporate phonological and visual similarity knowledge into language models for CSC via a specialized graph convolutional network (SpellGCN). The model builds a graph over the characters, and SpellGCN is learned to map this graph into a set of inter-dependent character classifiers. These classifiers are applied to the representations extracted by another network, such as BERT, enabling the whole network to be end-to-end trainable. Experiments are conducted on three human-annotated datasets. Our method achieves superior performance against previous models by a large margin.
Spelling Error Correction with Soft-Masked BERT
Authors: Shaohua Zhang, Haoran Huang, Jicong Liu, Hang Li
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Spelling error correction is an important yet challenging task because a satisfactory solution of it essentially needs human-level language understanding ability. Without loss of generality we consider Chinese spelling error correction (CSC) in this paper. A state-of-the-art method for the task selects a character from a list of candidates for correction (including non-correction) at each position of the sentence on the basis of BERT, the language representation model. The accuracy of the method can be sub-optimal, however, because BERT does not have sufficient capability to detect whether there is an error at each position, apparently due to the way of pre-training it using mask language modeling. In this work, we propose a novel neural architecture to address the aforementioned issue, which consists of a network for error detection and a network for error correction based on BERT, with the former being connected to the latter with what we call soft-masking technique. Our method of using \textquoteleftSoft-Masked BERT' is general, and it may be employed in other language detection-correction problems. Experimental results on two datasets, including one large dataset which we create and plan to release, demonstrate that the performance of our proposed method is significantly better than the baselines including the one solely based on BERT.
A Frame-based Sentence Representation for Machine Reading Comprehension
Authors: Shaoru Guo, Ru Li, Hongye Tan, Xiaoli Li, Yong Guan, Hongyan Zhao, Yueping Zhang
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Sentence representation (SR) is the most crucial and challenging task in Machine Reading Comprehension (MRC). MRC systems typically only utilize the information contained in the sentence itself, while human beings can leverage their semantic knowledge. To bridge the gap, we proposed a novel Frame-based Sentence Representation (FSR) method, which employs frame semantic knowledge to facilitate sentence modelling. Specifically, different from existing methods that only model lexical units (LUs), Frame Representation Models, which utilize both LUs in frame and Frame-to-Frame (F-to-F) relations, are designed to model frames and sentences with attention schema. Our proposed FSR method is able to integrate multiple-frame semantic information to get much better sentence representations. Our extensive experimental results show that it performs better than state-of-the-art technologies on machine reading comprehension task.
A Methodology for Creating Question Answering Corpora Using Inverse Data Annotation
Authors: Jan Deriu, Katsiaryna Mlynchyk, Philippe Schläpfer, Alvaro Rodrigo, Dirk von Grünigen, Nicolas Kaiser, Kurt Stockinger, Eneko Agirre, Mark Cieliebak
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: In this paper, we introduce a novel methodology to efficiently construct a corpus for question answering over structured data. For this, we introduce an intermediate representation that is based on the logical query plan in a database, called Operation Trees (OT). This representation allows us to invert the annotation process without loosing flexibility in the types of queries that we generate. Furthermore, it allows for fine-grained alignment of the tokens to the operations. Thus, we randomly generate OTs from a context free grammar and annotators just have to write the appropriate question and assign the tokens. We compare our corpus OTTA (Operation Trees and Token Assignment), a large semantic parsing corpus for evaluating natural language interfaces to databases, to Spider and LC-QuaD 2.0 and show that our methodology more than triples the annotation speed while maintaining the complexity of the queries. Finally, we train a state-of-the-art semantic parsing model on our data and show that our dataset is a challenging dataset and that the token alignment can be leveraged to significantly increase the performance.
Contextualized Sparse Representations for Real-Time Open-Domain Question Answering
Authors: Jinhyuk Lee, Minjoon Seo, Hannaneh Hajishirzi, Jaewoo Kang
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Open-domain question answering can be formulated as a phrase retrieval problem, in which we can expect huge scalability and speed benefit but often suffer from low accuracy due to the limitation of existing phrase representation models. In this paper, we aim to improve the quality of each phrase embedding by augmenting it with a contextualized sparse representation (Sparc). Unlike previous sparse vectors that are term-frequency-based (e.g., tf-idf) or directly learned (only few thousand dimensions), we leverage rectified self-attention to indirectly learn sparse vectors in n-gram vocabulary space. By augmenting the previous phrase retrieval model (Seo et al., 2019) with Sparc, we show 4%+ improvement in CuratedTREC and SQuAD-Open. Our CuratedTREC score is even better than the best known retrieve & read model with at least 45x faster inference speed.
Dynamic Sampling Strategies for Multi-Task Reading Comprehension
Authors: Ananth Gottumukkala, Dheeru Dua, Sameer Singh, Matt Gardner
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Building general reading comprehension systems, capable of solving multiple datasets at the same time, is a recent aspirational goal in the research community. Prior work has focused on model architecture or generalization to held out datasets, and largely passed over the particulars of the multi-task learning set up. We show that a simple dynamic sampling strategy, selecting instances for training proportional to the multi-task model`s current performance on a dataset relative to its single task performance, gives substantive gains over prior multi-task sampling strategies, mitigating the catastrophic forgetting that is common in multi-task learning. We also demonstrate that allowing instances of different tasks to be interleaved as much as possible between each epoch and batch has a clear benefit in multitask performance over forcing task homogeneity at the epoch or batch level. Our final model shows greatly increased performance over the best model on ORB, a recently-released multitask reading comprehension benchmark.
Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension
Authors: Fei Yuan, Linjun Shou, Xuanyu Bai, Ming Gong, Yaobo Liang, Nan Duan, Yan Fu, Daxin Jiang
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Multilingual pre-trained models could leverage the training data from a rich source language (such as English) to improve performance on low resource languages. However, the transfer quality for multilingual Machine Reading Comprehension (MRC) is significantly worse than sentence classification tasks mainly due to the requirement of MRC to detect the word level answer boundary. In this paper, we propose two auxiliary tasks in the fine-tuning stage to create additional phrase boundary supervision: (1) A mixed MRC task, which translates the question or passage to other languages and builds cross-lingual question-passage pairs; (2) A language-agnostic knowledge masking task by leveraging knowledge phrases mined from web. Besides, extensive experiments on two cross-lingual MRC datasets show the effectiveness of our proposed approach.
Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading
Authors: Yifan Gao, Chien-Sheng Wu, Shafiq Joty, Caiming Xiong, Richard Socher, Irwin King, Michael Lyu, Steven C.H. Hoi
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: The goal of conversational machine reading is to answer user questions given a knowledge base text which may require asking clarification questions. Existing approaches are limited in their decision making due to struggles in extracting question-related rules and reasoning about them. In this paper, we present a new framework of conversational machine reading that comprises a novel Explicit Memory Tracker (EMT) to track whether conditions listed in the rule text have already been satisfied to make a decision. Moreover, our framework generates clarification questions by adopting a coarse-to-fine reasoning strategy, utilizing sentence-level entailment scores to weight token-level distributions. On the ShARC benchmark (blind, held-out) testset, EMT achieves new state-of-the-art results of 74.6% micro-averaged decision accuracy and 49.5 BLEU4. We also show that EMT is more interpretable by visualizing the entailment-oriented reasoning process as the conversation flows. Code and models are released at r̆lhttps://github.com/Yifan-Gao/explicit_memory_tracker.
Injecting Numerical Reasoning Skills into Language Models
Authors: Mor Geva, Ankit Gupta, Jonathan Berant
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Large pre-trained language models (LMs) are known to encode substantial amounts of linguistic information. However, high-level reasoning skills, such as numerical reasoning, are difficult to learn from a language-modeling objective only. Consequently, existing models for numerical reasoning have used specialized architectures with limited flexibility. In this work, we show that numerical reasoning is amenable to automatic data generation, and thus one can inject this skill into pre-trained LMs, by generating large amounts of data, and training in a multi-task setup. We show that pre-training our model, GenBERT, on this data, dramatically improves performance on DROP (49.3 --\ensuremath> 72.3 F1), reaching performance that matches state-of-the-art models of comparable size, while using a simple and general-purpose encoder-decoder architecture. Moreover, GenBERT generalizes well to math word problem datasets, while maintaining high performance on standard RC tasks. Our approach provides a general recipe for injecting skills into large pre-trained LMs, whenever the skill is amenable to automatic data augmentation.
Learning to Identify Follow-Up Questions in Conversational Question Answering
Authors: Souvik Kundu, Qian Lin, Hwee Tou Ng
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Despite recent progress in conversational question answering, most prior work does not focus on follow-up questions. Practical conversational question answering systems often receive follow-up questions in an ongoing conversation, and it is crucial for a system to be able to determine whether a question is a follow-up question of the current conversation, for more effective answer finding subsequently. In this paper, we introduce a new follow-up question identification task. We propose a three-way attentive pooling network that determines the suitability of a follow-up question by capturing pair-wise interactions between the associated passage, the conversation history, and a candidate follow-up question. It enables the model to capture topic continuity and topic shift while scoring a particular candidate follow-up question. Experiments show that our proposed three-way attentive pooling network outperforms all baseline systems by significant margins.
Query Graph Generation for Answering Multi-hop Complex Questions from Knowledge Bases
Authors: Yunshi Lan, Jing Jiang
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Previous work on answering complex questions from knowledge bases usually separately addresses two types of complexity: questions with constraints and questions with multiple hops of relations. In this paper, we handle both types of complexity at the same time. Motivated by the observation that early incorporation of constraints into query graphs can more effectively prune the search space, we propose a modified staged query graph generation method with more flexible ways to generate query graphs. Our experiments clearly show that our method achieves the state of the art on three benchmark KBQA datasets.
A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers
Authors: Shen-yun Miao, Chao-Chun Liang, Keh-Yih Su
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: We present ASDiv (Academia Sinica Diverse MWP Dataset), a diverse (in terms of both language patterns and problem types) English math word problem (MWP) corpus for evaluating the capability of various MWP solvers. Existing MWP corpora for studying AI progress remain limited either in language usage patterns or in problem types. We thus present a new English MWP corpus with 2,305 MWPs that cover more text patterns and most problem types taught in elementary school. Each MWP is annotated with its problem type and grade level (for indicating the level of difficulty). Furthermore, we propose a metric to measure the lexicon usage diversity of a given MWP corpus, and demonstrate that ASDiv is more diverse than existing corpora. Experiments show that our proposed corpus reflects the true capability of MWP solvers more faithfully.
Improving Image Captioning Evaluation by Considering Inter References Variance
Authors: Yanzhi Yi, Hangyu Deng, Jinglu Hu
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Evaluating image captions is very challenging partially due to the fact that there are multiple correct captions for every single image. Most of the existing one-to-one metrics operate by penalizing mismatches between reference and generative caption without considering the intrinsic variance between ground truth captions. It usually leads to over-penalization and thus a bad correlation to human judgment. Recently, the latest one-to-one metric BERTScore can achieve high human correlation in system-level tasks while some issues can be fixed for better performance. In this paper, we propose a novel metric based on BERTScore that could handle such a challenge and extend BERTScore with a few new features appropriately for image captioning evaluation. The experimental results show that our metric achieves state-of-the-art human judgment correlation.
Revisiting the Context Window for Cross-lingual Word Embeddings
Authors: Ryokan Ri, Yoshimasa Tsuruoka
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Existing approaches to mapping-based cross-lingual word embeddings are based on the assumption that the source and target embedding spaces are structurally similar. The structures of embedding spaces largely depend on the co-occurrence statistics of each word, which the choice of context window determines. Despite this obvious connection between the context window and mapping-based cross-lingual embeddings, their relationship has been underexplored in prior work. In this work, we provide a thorough evaluation, in various languages, domains, and tasks, of bilingual embeddings trained with different context windows. The highlight of our findings is that increasing the size of both the source and target window sizes improves the performance of bilingual lexicon induction, especially the performance on frequent nouns.
Moving Down the Long Tail of Word Sense Disambiguation with Gloss Informed Bi-encoders
Authors: Terra Blevins, Luke Zettlemoyer
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: A major obstacle in Word Sense Disambiguation (WSD) is that word senses are not uniformly distributed, causing existing models to generally perform poorly on senses that are either rare or unseen during training. We propose a bi-encoder model that independently embeds (1) the target word with its surrounding context and (2) the dictionary definition, or gloss, of each sense. The encoders are jointly optimized in the same representation space, so that sense disambiguation can be performed by finding the nearest sense embedding for each target word embedding. Our system outperforms previous state-of-the-art models on English all-words WSD; these gains predominantly come from improved performance on rare senses, leading to a 31.1% error reduction on less frequent senses over prior work. This demonstrates that rare senses can be more effectively disambiguated by modeling their definitions.
Code-Switching Patterns Can Be an Effective Route to Improve Performance of Downstream NLP Applications: A Case Study of Humour, Sarcasm and Hate Speech Detection
Authors: Srijan Bansal, Vishal Garimella, Ayush Suhane, Jasabanta Patro, Animesh Mukherjee
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: In this paper, we demonstrate how code-switching patterns can be utilised to improve various downstream NLP applications. In particular, we encode various switching features to improve humour, sarcasm and hate speech detection tasks. We believe that this simple linguistic observation can also be potentially helpful in improving other similar NLP applications.