pwc-archive/papers-with-abstracts huggingface dataset:
Dialogue State Tracking with Explicit Slot Connection Modeling
Authors: Yawen Ouyang, Moxin Chen, Xin-yu Dai, Yinggong Zhao, Shu-Jian Huang, Jia-Jun Chen
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Dialogue State Tracking

Recent proposed approaches have made promising progress in dialogue state tracking (DST). However, in multi-domain scenarios, ellipsis and reference are frequently adopted by users to express values that have been mentioned by slots from other domains. To handle these phenomena, we propose a Dialogue State Tracking with Slot Connections (DST-SC) model to explicitly consider slot correlations across different domains. Given a target slot, the slot connecting mechanism in DST-SC can infer its source slot and copy the source slot value directly, thus significantly reducing the difficulty of learning and reasoning. Experimental results verify the benefits of explicit slot connection modeling, and our model achieves state-of-the-art performance on MultiWOZ 2.0 and MultiWOZ 2.1 datasets.
Generating Informative Conversational Response using Recurrent Knowledge-Interaction and Knowledge-Copy
Authors: Xiexiong Lin, Weiyu Jian, Jianshan He, Taifeng Wang, Wei Chu
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

Knowledge-driven conversation approaches have achieved remarkable research attention recently. However, generating an informative response with multiple relevant knowledge without losing fluency and coherence is still one of the main challenges. To address this issue, this paper proposes a method that uses recurrent knowledge interaction among response decoding steps to incorporate appropriate knowledge. Furthermore, we introduce a knowledge copy mechanism using a knowledge-aware pointer network to copy words from external knowledge according to knowledge attention distribution. Our joint neural conversation model which integrates recurrent Knowledge-Interaction and knowledge Copy (KIC) performs well on generating informative responses. Experiments demonstrate that our model with fewer parameters yields significant improvements over competitive baselines on two datasets Wizard-of-Wikipedia(average Bleu +87{\%}; abs.: 0.034) and DuConv(average Bleu +20{\%}; abs.: 0.047)) with different knowledge formats (textual {\&} structured) and different languages (English {\&} Chinese).
Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network
Authors: Yangming Li, Kaisheng Yao, Libo Qin, Wanxiang Che, Xiaolong Li, Ting Liu
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Hallucination, Task-Oriented Dialogue Systems, Text Generation

Data-driven approaches using neural networks have achieved promising performances in natural language generation (NLG). However, neural generators are prone to make mistakes, e.g., neglecting an input slot value and generating a redundant slot value. Prior works refer this to hallucination phenomenon. In this paper, we study slot consistency for building reliable NLG systems with all slot values of input dialogue act (DA) properly generated in output sentences. We propose Iterative Rectification Network (IRN) for improving general NLG systems to produce both correct and fluent responses. It applies a bootstrapping algorithm to sample training candidates and uses reinforcement learning to incorporate discrete reward related to slot inconsistency into training. Comprehensive studies have been conducted on multiple benchmark datasets, showing that the proposed methods have significantly reduced the slot error rate (ERR) for all strong baselines. Human evaluations also have confirmed its effectiveness.
A Complete Shift-Reduce Chinese Discourse Parser with Robust Dynamic Oracle
Authors: Shyh-Shiun Hung, Hen-Hsen Huang, Hsin-Hsi Chen
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Data Augmentation, Discourse Parsing

This work proposes a standalone, complete Chinese discourse parser for practical applications. We approach Chinese discourse parsing from a variety of aspects and improve the shift-reduce parser not only by integrating the pre-trained text encoder, but also by employing novel training strategies. We revise the dynamic-oracle procedure for training the shift-reduce parser, and apply unsupervised data augmentation to enhance rhetorical relation recognition. Experimental results show that our Chinese discourse parser achieves the state-of-the-art performance.
TransS-Driven Joint Learning Architecture for Implicit Discourse Relation Recognition
Authors: Ruifang He, Jian Wang, Fengyu Guo, Yugui Han
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Relation

Implicit discourse relation recognition is a challenging task due to the lack of connectives as strong linguistic clues. Previous methods primarily encode two arguments separately or extract the specific interaction patterns for the task, which have not fully exploited the annotated relation signal. Therefore, we propose a novel TransS-driven joint learning architecture to address the issues. Specifically, based on the multi-level encoder, we 1) translate discourse relations in low-dimensional embedding space (called TransS), which could mine the latent geometric structure information of argument-relation instances; 2) further exploit the semantic features of arguments to assist discourse understanding; 3) jointly learn 1) and 2) to mutually reinforce each other to obtain the better argument representations, so as to improve the performance of the task. Extensive experimental results on the Penn Discourse TreeBank (PDTB) show that our model achieves competitive results against several state-of-the-art systems.
Learning to Ask More: Semi-Autoregressive Sequential Question Generation under Dual-Graph Interaction
Authors: Zi Chai, Xiaojun Wan
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Question Generation, Question-Generation

Traditional Question Generation (TQG) aims to generate a question given an input passage and an answer. When there is a sequence of answers, we can perform Sequential Question Generation (SQG) to produce a series of interconnected questions. Since the frequently occurred information omission and coreference between questions, SQG is rather challenging. Prior works regarded SQG as a dialog generation task and recurrently produced each question. However, they suffered from problems caused by error cascades and could only capture limited context dependencies. To this end, we generate questions in a semi-autoregressive way. Our model divides questions into different groups and generates each group of them in parallel. During this process, it builds two graphs focusing on information from passages, answers respectively and performs dual-graph interaction to get information for generation. Besides, we design an answer-aware attention mechanism and the coarse-to-fine generation scenario. Experiments on our new dataset containing 81.9K questions show that our model substantially outperforms prior works.
A Joint Model for Document Segmentation and Segment Labeling
Authors: Joe Barrow, Rajiv Jain, Vlad Morariu, Varun Manjunatha, Douglas Oard, Philip Resnik
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Segmentation, Text Segmentation

Text segmentation aims to uncover latent structure by dividing text from a document into coherent sections. Where previous work on text segmentation considers the tasks of document segmentation and segment labeling separately, we show that the tasks contain complementary information and are best addressed jointly. We introduce Segment Pooling LSTM (S-LSTM), which is capable of jointly segmenting a document and labeling segments. In support of joint training, we develop a method for teaching the model to recover from errors by aligning the predicted and ground truth segments. We show that S-LSTM reduces segmentation error by 30{\%} on average, while also improving segment labeling.
Contextualized Weak Supervision for Text Classification
Authors: Dheeraj Mekala, Jingbo Shang
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Classification, General Classification, text-classification, Text Classification

Weakly supervised text classification based on a few user-provided seed words has recently attracted much attention from researchers. Existing methods mainly generate pseudo-labels in a context-free manner (e.g., string matching), therefore, the ambiguous, context-dependent nature of human language has been long overlooked. In this paper, we propose a novel framework ConWea, providing contextualized weak supervision for text classification. Specifically, we leverage contextualized representations of word occurrences and seed word information to automatically differentiate multiple interpretations of the same word, and thus create a contextualized corpus. This contextualized corpus is further utilized to train the classifier and expand seed words in an iterative manner. This process not only adds new contextualized, highly label-indicative keywords but also disambiguates initial seed words, making our weak supervision fully contextualized. Extensive experiments and case studies on real-world datasets demonstrate the necessity and significant advantages of using contextualized weak supervision, especially when the class labels are fine-grained.
Text Classification with Negative Supervision
Authors: Sora Ohashi, Junya Takayama, Tomoyuki Kajiwara, Chenhui Chu, Yuki Arase
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Classification, General Classification, Semantic Similarity, Semantic Textual Similarity, Sentence, text-classification, Text Classification

Advanced pre-trained models for text representation have achieved state-of-the-art performance on various text classification tasks. However, the discrepancy between the semantic similarity of texts and labelling standards affects classifiers, i.e. leading to lower performance in cases where classifiers should assign different labels to semantically similar texts. To address this problem, we propose a simple multitask learning model that uses negative supervision. Specifically, our model encourages texts with different labels to have distinct representations. Comprehensive experiments show that our model outperforms the state-of-the-art pre-trained model on both single- and multi-label classifications, sentence and document classifications, and classifications in three different languages.
Content Word Aware Neural Machine Translation
Authors: Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Machine Translation, NMT, Sentence, Translation

Neural machine translation (NMT) encodes the source sentence in a universal way to generate the target sentence word-by-word. However, NMT does not consider the importance of word in the sentence meaning, for example, some words (i.e., content words) express more important meaning than others (i.e., function words). To address this limitation, we first utilize word frequency information to distinguish between content and function words in a sentence, and then design a content word-aware NMT to improve translation performance. Empirical results on the WMT14 English-to-German, WMT14 English-to-French, and WMT17 Chinese-to-English translation tasks show that the proposed methods can significantly improve the performance of Transformer-based NMT.
Jointly Masked Sequence-to-Sequence Model for Non-Autoregressive Neural Machine Translation
Authors: Junliang Guo, Linli Xu, Enhong Chen
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Decoder, Language Modeling, Language Modelling, Machine Translation, Translation

The masked language model has received remarkable attention due to its effectiveness on various natural language processing tasks. However, few works have adopted this technique in the sequence-to-sequence models. In this work, we introduce a jointly masked sequence-to-sequence model and explore its application on non-autoregressive neural machine translation{\textasciitilde}(NAT). Specifically, we first empirically study the functionalities of the encoder and the decoder in NAT models, and find that the encoder takes a more important role than the decoder regarding the translation quality. Therefore, we propose to train the encoder more rigorously by masking the encoder input while training. As for the decoder, we propose to train it based on the consecutive masking of the decoder input with an $n$-gram loss function to alleviate the problem of translating duplicate words. The two types of masks are applied to the model jointly at the training stage. We conduct experiments on five benchmark machine translation tasks, and our model can achieve 27.69/32.24 BLEU scores on WMT14 English-German/German-English tasks with $5+$ times speed up compared with an autoregressive model.
A Three-Parameter Rank-Frequency Relation in Natural Languages
Authors: Chenchen Ding, Masao Utiyama, Eiichiro Sumita
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Relation

We present that, the rank-frequency relation in textual data follows $f \propto r^{-\alpha}(r+\gamma)^{-\beta}$, where $f$ is the token frequency and $r$ is the rank by frequency, with ($\alpha$, $\beta$, $\gamma$) as parameters. The formulation is derived based on the empirical observation that $d^2 (x+y)/dx^2$ is a typical impulse function, where $(x,y)=(\log r, \log f)$. The formulation is the power law when $\beta=0$ and the Zipf{--}Mandelbrot law when $\alpha=0$. We illustrate that $\alpha$ is related to the analytic features of syntax and $\beta+\gamma$ to those of morphology in natural languages from an investigation of multilingual corpora.
Emergence of Syntax Needs Minimal Supervision
Authors: Rapha{\"e}l Bailly, Kata G{\'a}bor
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

This paper is a theoretical contribution to the debate on the learnability of syntax from a corpus without explicit syntax-specific guidance. Our approach originates in the observable structure of a corpus, which we use to define and isolate grammaticality (syntactic information) and meaning/pragmatics information. We describe the formal characteristics of an autonomous syntax and show that it becomes possible to search for syntax-based lexical categories with a simple optimization process, without any prior hypothesis on the form of the model.
Predicting the Topical Stance and Political Leaning of Media using Tweets
Authors: Peter Stefanov, Kareem Darwish, Atanas Atanasov, Preslav Nakov
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

Discovering the stances of media outlets and influential people on current, debatable topics is important for social statisticians and policy makers. Many supervised solutions exist for determining viewpoints, but manually annotating training data is costly. In this paper, we propose a cascaded method that uses unsupervised learning to ascertain the stance of Twitter users with respect to a polarizing topic by leveraging their retweet behavior; then, it uses supervised learning based on user labels to characterize both the general political leaning of online media and of popular Twitter users, as well as their stance with respect to the target polarizing topic. We evaluate the model by comparing its predictions to gold labels from the Media Bias/Fact Check website, achieving 82.6{\%} accuracy.
End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2
Authors: Donghoon Ham, Jeong-Gwan Lee, Youngsoo Jang, Kee-Eung Kim
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Goal-Oriented Dialogue Systems

The goal-oriented dialogue system needs to be optimized for tracking the dialogue flow and carrying out an effective conversation under various situations to meet the user goal. The traditional approach to build such a dialogue system is to take a pipelined modular architecture, where its modules are optimized individually. However, such an optimization scheme does not necessarily yield the overall performance improvement of the whole system. On the other hand, end-to-end dialogue systems with monolithic neural architecture are often trained only with input-output utterances, without taking into account the entire annotations available in the corpus. This scheme makes it difficult for goal-oriented dialogues where the system needs to integrate with external systems or to provide interpretable information about why the system generated a particular response. In this paper, we present an end-to-end neural architecture for dialogue systems that addresses both challenges above. In the human evaluation, our dialogue system achieved the success rate of 68.32{\%}, the language understanding score of 4.149, and the response appropriateness score of 4.287, which ranked the system at the top position in the end-to-end multi-domain dialogue system task in the 8th dialogue systems technology challenge (DSTC8).
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
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Dialog Learning, Goal-Oriented Dialog, Meta-Learning

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
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Graph Attention, TAG

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.
Explicit Semantic Decomposition for Definition Generation
Authors: Jiahuan Li, Yu Bao, Shu-Jian Huang, Xin-yu Dai, Jia-Jun Chen
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

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.
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
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: AMR-to-Text Generation, Graph Attention, Graph-to-Sequence, Text Generation

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.
An Online Semantic-enhanced Dirichlet Model for Short Text Stream Clustering
Authors: Jay Kumar, Junming Shao, Salah Uddin, Wazir Ali
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Clustering, Short Text Clustering, Text Clustering

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.
Interactive Construction of User-Centric Dictionary for Text Analytics
Authors: Ryosuke Kohita, Issei Yoshida, Hiroshi Kanayama, Tetsuya Nasukawa
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

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
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: model

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.