pwc-archive/papers-with-abstracts huggingface dataset:
A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis
Authors: Qingnan Jiang, Lei Chen, Ruifeng Xu, Xiang Ao, Min Yang
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentence, Sentiment Analysis

Aspect-based sentiment analysis (ABSA) has attracted increasing attention recently due to its broad applications. In existing ABSA datasets, most sentences contain only one aspect or multiple aspects with the same sentiment polarity, which makes ABSA task degenerate to sentence-level sentiment analysis. In this paper, we present a new large-scale Multi-Aspect Multi-Sentiment (MAMS) dataset, in which each sentence contains at least two different aspects with different sentiment polarities. The release of this dataset would push forward the research in this field. In addition, we propose simple yet effective CapsNet and CapsNet-BERT models which combine the strengths of recent NLP advances. Experiments on our new dataset show that the proposed model significantly outperforms the state-of-the-art baseline methods
Multi-Task Stance Detection with Sentiment and Stance Lexicons
Authors: Yingjie Li, Cornelia Caragea
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Sentiment Analysis, Sentiment Classification, Stance Detection

Stance detection aims to detect whether the opinion holder is in support of or against a given target. Recent works show improvements in stance detection by using either the attention mechanism or sentiment information. In this paper, we propose a multi-task framework that incorporates target-specific attention mechanism and at the same time takes sentiment classification as an auxiliary task. Moreover, we used a sentiment lexicon and constructed a stance lexicon to provide guidance for the attention layer. Experimental results show that the proposed model significantly outperforms state-of-the-art deep learning methods on the SemEval-2016 dataset.
A Robust Self-Learning Framework for Cross-Lingual Text Classification
Authors: Xin Dong, Gerard de Melo
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Classification, General Classification, Self-Learning, Sentiment Analysis, Sentiment Classification, text-classification, Text Classification

Based on massive amounts of data, recent pretrained contextual representation models have made significant strides in advancing a number of different English NLP tasks. However, for other languages, relevant training data may be lacking, while state-of-the-art deep learning methods are known to be data-hungry. In this paper, we present an elegantly simple robust self-learning framework to include unlabeled non-English samples in the fine-tuning process of pretrained multilingual representation models. We leverage a multilingual model{'}s own predictions on unlabeled non-English data in order to obtain additional information that can be used during further fine-tuning. Compared with original multilingual models and other cross-lingual classification models, we observe significant gains in effectiveness on document and sentiment classification for a range of diverse languages.
Learning to Flip the Sentiment of Reviews from Non-Parallel Corpora
Authors: Canasai Kruengkrai
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Sentence

Flipping sentiment while preserving sentence meaning is challenging because parallel sentences with the same content but different sentiment polarities are not always available for model learning. We introduce a method for acquiring imperfectly aligned sentences from non-parallel corpora and propose a model that learns to minimize the sentiment and content losses in a fully end-to-end manner. Our model is simple and offers well-balanced results across two domains: Yelp restaurant and Amazon product reviews.
Label Embedding using Hierarchical Structure of Labels for Twitter Classification
Authors: Taro Miyazaki, Kiminobu Makino, Yuka Takei, Hiroki Okamoto, Jun Goto
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Classification, General Classification, Retrieval, Text Retrieval

Twitter is used for various applications such as disaster monitoring and news material gathering. In these applications, each Tweet is classified into pre-defined classes. These classes have a semantic relationship with each other and can be classified into a hierarchical structure, which is regarded as important information. Label texts of pre-defined classes themselves also include important clues for classification. Therefore, we propose a method that can consider the hierarchical structure of labels and label texts themselves. We conducted evaluation over the Text REtrieval Conference (TREC) 2018 Incident Streams (IS) track dataset, and we found that our method outperformed the methods of the conference participants.
Adversarial Removal of Demographic Attributes Revisited
Authors: Maria Barrett, Yova Kementchedjhieva, Yanai Elazar, Desmond Elliott, Anders S{\o}gaard
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Diagnostic

Elazar and Goldberg (2018) showed that protected attributes can be extracted from the representations of a debiased neural network for mention detection at above-chance levels, by evaluating a diagnostic classifier on a held-out subsample of the data it was trained on. We revisit their experiments and conduct a series of follow-up experiments showing that, in fact, the diagnostic classifier generalizes poorly to both new in-domain samples and new domains, indicating that it relies on correlations specific to their particular data sample. We further show that a diagnostic classifier trained on the biased baseline neural network also does not generalize to new samples. In other words, the biases detected in Elazar and Goldberg (2018) seem restricted to their particular data sample, and would therefore not bias the decisions of the model on new samples, whether in-domain or out-of-domain. In light of this, we discuss better methodologies for detecting bias in our models.
A deep-learning framework to detect sarcasm targets
Authors: Jasabanta Patro, Srijan Bansal, Animesh Mukherjee
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Deep Learning, Opinion Mining, Sentiment Analysis

In this paper we propose a deep learning framework for sarcasm target detection in predefined sarcastic texts. Identification of sarcasm targets can help in many core natural language processing tasks such as aspect based sentiment analysis, opinion mining etc. To begin with, we perform an empirical study of the socio-linguistic features and identify those that are statistically significant in indicating sarcasm targets (p-values in the range(0.05,0.001)). Finally, we present a deep-learning framework augmented with socio-linguistic features to detect sarcasm targets in sarcastic book-snippets and tweets.We achieve a huge improvement in the performance in terms of exact match and dice scores compared to the current state-of-the-art baseline.
Incorporating Label Dependencies in Multilabel Stance Detection
Authors: William Ferreira, Andreas Vlachos
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: General Classification, Stance Detection

Stance detection in social media is a well-studied task in a variety of domains. Nevertheless, previous work has mostly focused on multiclass versions of the problem, where the labels are mutually exclusive, and typically positive, negative or neutral. In this paper, we address versions of the task in which an utterance can have multiple labels, thus corresponding to multilabel classification. We propose a method that explicitly incorporates label dependencies in the training objective and compare it against a variety of baselines, as well as a reduction of multilabel to multiclass learning. In experiments with three datasets, we find that our proposed method improves upon all baselines on two out of three datasets. We also show that the reduction of multilabel to multiclass classification can be very competitive, especially in cases where the output consists of a small number of labels and one can enumerate over all label combinations.
Embedding Lexical Features via Tensor Decomposition for Small Sample Humor Recognition
Authors: Zhenjie Zhao, Andrew Cattle, Evangelos Papalexakis, Xiaojuan Ma
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Tensor Decomposition

We propose a novel tensor embedding method that can effectively extract lexical features for humor recognition. Specifically, we use word-word co-occurrence to encode the contextual content of documents, and then decompose the tensor to get corresponding vector representations. We show that this simple method can capture features of lexical humor effectively for continuous humor recognition. In particular, we achieve a distance of 0.887 on a global humor ranking task, comparable to the top performing systems from SemEval 2017 Task 6B (Potash et al., 2017) but without the need for any external training corpus. In addition, we further show that this approach is also beneficial for small sample humor recognition tasks through a semi-supervised label propagation procedure, which achieves about 0.7 accuracy on the 16000 One-Liners (Mihalcea and Strapparava, 2005) and Pun of the Day (Yang et al., 2015) humour classification datasets using only 10{\%} of known labels.
Neural News Recommendation with Multi-Head Self-Attention
Authors: Chuhan Wu, Fangzhao Wu, Suyu Ge, Tao Qi, Yongfeng Huang, Xing Xie
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: News Recommendation, Recommendation Systems

News recommendation can help users find interested news and alleviate information overload. Precisely modeling news and users is critical for news recommendation, and capturing the contexts of words and news is important to learn news and user representations. In this paper, we propose a neural news recommendation approach with multi-head self-attention (NRMS). The core of our approach is a news encoder and a user encoder. In the news encoder, we use multi-head self-attentions to learn news representations from news titles by modeling the interactions between words. In the user encoder, we learn representations of users from their browsed news and use multi-head self-attention to capture the relatedness between the news. Besides, we apply additive attention to learn more informative news and user representations by selecting important words and news. Experiments on a real-world dataset validate the effectiveness and efficiency of our approach.
Telling the Whole Story: A Manually Annotated Chinese Dataset for the Analysis of Humor in Jokes
Authors: Dongyu Zhang, Heting Zhang, Xikai Liu, Hongfei Lin, Feng Xia
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11

Humor plays important role in human communication, which makes it important problem for natural language processing. Prior work on the analysis of humor focuses on whether text is humorous or not, or the degree of funniness, but this is insufficient to explain why it is funny. We therefore create a dataset on humor with 9,123 manually annotated jokes in Chinese. We propose a novel annotation scheme to give scenarios of how humor arises in text. Specifically, our annotations of linguistic humor not only contain the degree of funniness, like previous work, but they also contain key words that trigger humor as well as character relationship, scene, and humor categories. We report reasonable agreement between annota-tors. We also conduct an analysis and exploration of the dataset. To the best of our knowledge, we are the first to approach humor annotation for exploring the underlying mechanism of the use of humor, which may contribute to a significantly deeper analysis of humor. We also contribute with a scarce and valuable dataset, which we will release publicly.
Generating Natural Anagrams: Towards Language Generation Under Hard Combinatorial Constraints
Authors: Masaaki Nishino, Sho Takase, Tsutomu Hirao, Masaaki Nagata
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Sentence, Text Generation

An anagram is a sentence or a phrase that is made by permutating the characters of an input sentence or a phrase. For example, {``}Trims cash{''} is an anagram of {``}Christmas{''}. Existing automatic anagram generation methods can find possible combinations of words form an anagram. However, they do not pay much attention to the naturalness of the generated anagrams. In this paper, we show that simple depth-first search can yield natural anagrams when it is combined with modern neural language models. Human evaluation results show that the proposed method can generate significantly more natural anagrams than baseline methods.
Cross-lingual intent classification in a low resource industrial setting
Authors: Talaat Khalil, Kornel Kie{\l}czewski, Georgios Christos Chouliaras, Amina Keldibek, Maarten Versteegh
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Classification, Cross-Lingual Transfer, General Classification, intent-classification, Intent Classification

This paper explores different approaches to multilingual intent classification in a low resource setting. Recent advances in multilingual text representations promise cross-lingual transfer for classifiers. We investigate the potential for this transfer in an applied industrial setting and compare to multilingual classification using machine translated text. Our results show that while the recently developed methods show promise, practical application calls for a combination of techniques for useful results.
SoftRegex: Generating Regex from Natural Language Descriptions using Softened Regex Equivalence
Authors: Jun-U Park, Sang-Ki Ko, Marco Cognetta, Yo-Sub Han
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11

We continue the study of generating se-mantically correct regular expressions from natural language descriptions (NL). The current state-of-the-art model SemRegex produces regular expressions from NLs by rewarding the reinforced learning based on the semantic (rather than syntactic) equivalence between two regular expressions. Since the regular expression equivalence problem is PSPACE-complete, we introduce the EQ{\_}Reg model for computing the simi-larity of two regular expressions using deep neural networks. Our EQ{\_}Reg mod-el essentially softens the equivalence of two regular expressions when used as a reward function. We then propose a new regex generation model, SoftRegex, us-ing the EQ{\_}Reg model, and empirically demonstrate that SoftRegex substantially reduces the training time (by a factor of at least 3.6) and produces state-of-the-art results on three benchmark datasets.
Spelling-Aware Construction of Macaronic Texts for Teaching Foreign-Language Vocabulary
Authors: Adithya Renduchintala, Philipp Koehn, Jason Eisner
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Language Modeling, Language Modelling

We present a machine foreign-language teacher that modifies text in a student{'}s native language (L1) by replacing some word tokens with glosses in a foreign language (L2), in such a way that the student can acquire L2 vocabulary simply by reading the resulting macaronic text. The machine teacher uses no supervised data from human students. Instead, to guide the machine teacher{'}s choice of which words to replace, we equip a cloze language model with a training procedure that can incrementally learn representations for novel words, and use this model as a proxy for the word guessing and learning ability of real human students. We use Mechanical Turk to evaluate two variants of the student model: (i) one that generates a representation for a novel word using only surrounding context and (ii) an extension that also uses the spelling of the novel word.
Towards Machine Reading for Interventions from Humanitarian-Assistance Program Literature
Authors: Bonan Min, Yee Seng Chan, Haoling Qiu, Joshua Fasching
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Humanitarian, Reading Comprehension

Solving long-lasting problems such as food insecurity requires a comprehensive understanding of interventions applied by governments and international humanitarian assistance organizations, and their results and consequences. Towards achieving this grand goal, a crucial first step is to extract past interventions and when and where they have been applied, from hundreds of thousands of reports automatically. In this paper, we developed a corpus annotated with interventions to foster research, and developed an information extraction system for extracting interventions and their location and time from text. We demonstrate early, very encouraging results on extracting interventions.
Context-Aware Conversation Thread Detection in Multi-Party Chat
Authors: Ming Tan, Dakuo Wang, Yupeng Gao, Haoyu Wang, Saloni Potdar, Xiaoxiao Guo, Shiyu Chang, Mo Yu
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11

In multi-party chat, it is common for multiple conversations to occur concurrently, leading to intermingled conversation threads in chat logs. In this work, we propose a novel Context-Aware Thread Detection (CATD) model that automatically disentangles these conversation threads. We evaluate our model on four real-world datasets and demonstrate an overall im-provement in thread detection accuracy over state-of-the-art benchmarks.
Applying BERT to Document Retrieval with Birch
Authors: Zeynep Akkalyoncu Yilmaz, Shengjin Wang, Wei Yang, Haotian Zhang, Jimmy Lin
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Information Retrieval, Retrieval

We present Birch, a system that applies BERT to document retrieval via integration with the open-source Anserini information retrieval toolkit to demonstrate end-to-end search over large document collections. Birch implements simple ranking models that achieve state-of-the-art effectiveness on standard TREC newswire and social media test collections. This demonstration focuses on technical challenges in the integration of NLP and IR capabilities, along with the design rationale behind our approach to tightly-coupled integration between Python (to support neural networks) and the Java Virtual Machine (to support document retrieval using the open-source Lucene search library). We demonstrate integration of Birch with an existing search interface as well as interactive notebooks that highlight its capabilities in an easy-to-understand manner.
Automatic Taxonomy Induction and Expansion
Authors: Nicolas Rodolfo Fauceglia, Alfio Gliozzo, Sarthak Dash, Md. Faisal Mahbub Chowdhury, N Mihindukulasooriya, ana
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11

The Knowledge Graph Induction Service (KGIS) is an end-to-end knowledge induction system. One of its main capabilities is to automatically induce taxonomies from input documents using a hybrid approach that takes advantage of linguistic patterns, semantic web and neural networks. KGIS allows the user to semi-automatically curate and expand the induced taxonomy through a component called Smart SpreadSheet by exploiting distributional semantics. In this paper, we describe these taxonomy induction and expansion features of KGIS. A screencast video demonstrating the system is available in https://ibm.box.com/v/emnlp-2019-demo .
Chameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech
Authors: Yuanfeng Song, Di Jiang, Weiwei Zhao, Qian Xu, Raymond Chi-Wing Wong, Qiang Yang
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Language model is a vital component in modern automatic speech recognition (ASR) systems. Since {``}one-size-fits-all{''} language model works suboptimally for conversational speeches, language model adaptation (LMA) is considered as a promising solution for solving this problem. In order to compare the state-of-the-art LMA techniques and systematically demonstrate their effect in conversational speech recognition, we develop a novel toolkit named Chameleon, which includes the state-of-the-art cache-based and topic-based LMA techniques. This demonstration does not only vividly visualize underlying working mechanisms of a variety of the state-of-the-art LMA models but also provide an interface for the user to customize the hyperparameters of them. With this demonstration, the audience can experience the effect of LMA in an interactive and real-time fashion. We wish this demonstration would inspire more research on better language model techniques for ASR.
Controlling Sequence-to-Sequence Models - A Demonstration on Neural-based Acrostic Generator
Authors: Liang-Hsin Shen, Pei-Lun Tai, Chao-Chung Wu, Shou-De Lin
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Text Generation

An acrostic is a form of writing that the first token of each line (or other recurring features in the text) forms a meaningful sequence. In this paper we present a generalized acrostic generation system that can hide certain message in a flexible pattern specified by the users. Different from previous works that focus on rule-based solutions, here we adopt a neural- based sequence-to-sequence model to achieve this goal. Besides acrostic, users are also allowed to specify the rhyme and length of the output sequences. Based on our knowledge, this is the first neural-based natural language generation system that demonstrates the capability of performing micro-level control over output sentences.
Entity resolution for noisy ASR transcripts
Authors: Arushi Raghuvanshi, Vijay Ramakrishnan, Varsha Embar, Lucien Carroll, Karthik Raghunathan
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Entity Resolution, Information Retrieval, Natural Language Understanding, Retrieval, speech-recognition, Speech Recognition

Large vocabulary domain-agnostic Automatic Speech Recognition (ASR) systems often mistranscribe domain-specific words and phrases. Since these generic ASR systems are the first component of most voice assistants in production, building Natural Language Understanding (NLU) systems that are robust to these errors can be a challenging task. In this paper, we focus on handling ASR errors in named entities, specifically person names, for a voice-based collaboration assistant. We demonstrate an effective method for resolving person names that are mistranscribed by black-box ASR systems, using character and phoneme-based information retrieval techniques and contextual information, which improves accuracy by 40.8{\%} on our production system. We provide a live interactive demo to further illustrate the nuances of this problem and the effectiveness of our solution.
EUSP: An Easy-to-Use Semantic Parsing PlatForm
Authors: Bo An, Chen Bo, Xianpei Han, Le Sun
Date: 2019-11-01 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Semantic Parsing

Semantic parsing aims to map natural language utterances into structured meaning representations. We present a modular platform, EUSP (Easy-to-Use Semantic Parsing PlatForm), that facilitates developers to build semantic parser from scratch. Instead of requiring a large amount of training data or complex grammar knowledge, in our platform developers can build grammar-based semantic parser or neural-based semantic parser through configure files which specify the modules and components that compose semantic parsing system. A high quality grammar-based semantic parsing system only requires domain lexicons rather than costly training data for a semantic parser. Furthermore, we provide a browser-based method to generate the semantic parsing system to minimize the difficulty of development. Experimental results show that the neural-based semantic parser system achieves competitive performance on semantic parsing task, and grammar-based semantic parsers significantly improve the performance of a business search engine.