pwc-archive/papers-with-abstracts huggingface dataset:
Semi-supervised Category-specific Review Tagging on Indonesian E-Commerce Product Reviews
Authors: Meng Sun, Marie Stephen Leo, Eram Munawwar, Paul C. Condylis, Sheng-yi Kong, Seong Per Lee, Albert Hidayat, Muhamad Danang Kerianto
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: TAG

Product reviews are a huge source of natural language data in e-commerce applications. Several millions of customers write reviews regarding a variety of topics. We categorize these topics into two groups as either {``}category-specific{''} topics or as {``}generic{''} topics that span multiple product categories. While we can use a supervised learning approach to tag review text for generic topics, it is impossible to use supervised approaches to tag category-specific topics due to the sheer number of possible topics for each category. In this paper, we present an approach to tag each review with several product category-specific tags on Indonesian language product reviews using a semi-supervised approach. We show that our proposed method can work at scale on real product reviews at Tokopedia, a major e-commerce platform in Indonesia. Manual evaluation shows that the proposed method can efficiently generate category-specific product tags.
SimsterQ: A Similarity based Clustering Approach to Opinion Question Answering
Authors: Aishwarya Ashok, Ganapathy Natarajan, Ramez Elmasri, Laurel Smith-Stvan
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Clustering, Question Answering, Sentence

In recent years, there has been an increase in online shopping resulting in an increased number of online reviews. Customers cannot delve into the huge amount of data when they are looking for specific aspects of a product. Some of these aspects can be extracted from the product reviews. In this paper we introduced SimsterQ - a clustering based system for answering questions that makes use of word vectors. Clustering was performed using cosine similarity scores between sentence vectors of reviews and questions. Two variants (Sim and Median) with and without stopwords were evaluated against traditional methods that use term frequency. We also used an n-gram approach to study the effect of noise. We used the reviews in the Amazon Reviews dataset to pick the answers. Evaluation was performed both at the individual sentence level using the top sentence from Okapi BM25 as the gold standard and at the whole answer level using review snippets as the gold standard. At the sentence level our system performed slightly better than a more complicated deep learning method. Our system returned answers similar to the review snippets from the Amazon QA Dataset as measured by the cosine similarity. Analysis was also performed on the quality of the clusters generated by our system.
e-Commerce and Sentiment Analysis: Predicting Outcomes of Class Action Lawsuits
Authors: Stacey Taylor, Vlado Keselj
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Management, Recommendation Systems, Sentiment Analysis

In recent years, the focus of e-Commerce research has been on better understanding the relationship between the internet marketplace, customers, and goods and services. This has been done by examining information that can be gleaned from consumer information, recommender systems, click rates, or the way purchasers go about making buying decisions, for example. This paper takes a very different approach and examines the companies themselves. In the past ten years, e-Commerce giants such as Amazon, Skymall, Wayfair, and Groupon have been embroiled in class action security lawsuits promulgated under Rule 10b(5), which, in short, is one of the Securities and Exchange Commission{'}s main rules surrounding fraud. Lawsuits are extremely expensive to the company and can damage a company{'}s brand extensively, with the shareholders left to suffer the consequences. We examined the Management Discussion and Analysis and the Market Risks for 96 companies using sentiment analysis on selected financial measures and found that we were able to predict the outcome of the lawsuits in our dataset using sentiment (tone) alone to a recall of 0.8207 using the Random Forest classifier. We believe that this is an important contribution as it has cross-domain implications and potential, and opens up new areas of research in e-Commerce, finance, and law, as the settlements from the class action lawsuits in our dataset alone are in excess of {\$}1.6 billion dollars, in aggregate.
On Application of Bayesian Parametric and Non-parametric Methods for User Cohorting in Product Search
Authors: Shashank Gupta
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Clustering

In this paper, we study the applicability of Bayesian Parametric and Non-parametric methods for user clustering in an E-commerce search setting. To the best of our knowledge, this is the first work that presents a comparative study of various Bayesian clustering methods in the context of product search. Specifically, we cluster users based on their topical patterns from their respective product search queries. To evaluate the quality of the clusters formed, we perform a collaborative query recommendation task. Our findings indicate that simple parametric model like Latent Dirichlet Allocation (LDA) outperforms more sophisticated non-parametric methods like Distance Dependent Chinese Restaurant Process and Dirichlet Process-based clustering in both tasks.
Simple Compounded-Label Training for Fact Extraction and Verification
Authors: Yixin Nie, Lisa Bauer, Mohit Bansal
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Claim Verification, Fact Checking, Misinformation, Multi-Task Learning, Retrieval, Sentence

Automatic fact checking is an important task motivated by the need for detecting and preventing the spread of misinformation across the web. The recently released FEVER challenge provides a benchmark task that assesses systems{'} capability for both the retrieval of required evidence and the identification of authentic claims. Previous approaches share a similar pipeline training paradigm that decomposes the task into three subtasks, with each component built and trained separately. Although achieving acceptable scores, these methods induce difficulty for practical application development due to unnecessary complexity and expensive computation. In this paper, we explore the potential of simplifying the system design and reducing training computation by proposing a joint training setup in which a single sequence matching model is trained with compounded labels that give supervision for both sentence selection and claim verification subtasks, eliminating the duplicate computation that occurs when models are designed and trained separately. Empirical results on FEVER indicate that our method: (1) outperforms the typical multi-task learning approach, and (2) gets comparable results to top performing systems with a much simpler training setup and less training computation (in terms of the amount of data consumed and the number of model parameters), facilitating future works on the automatic fact checking task and its practical usage.
Developing a How-to Tip Machine Comprehension Dataset and its Evaluation in Machine Comprehension by BERT
Authors: Tengyang Chen, Hongyu Li, Miho Kasamatsu, Takehito Utsuro, Yasuhide Kawada
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Question Answering, Reading Comprehension

In the field of factoid question answering (QA), it is known that the state-of-the-art technology has achieved an accuracy comparable to that of humans in a certain benchmark challenge. On the other hand, in the area of non-factoid QA, there is still a limited number of datasets for training QA models, i.e., machine comprehension models. Considering such a situation within the field of the non-factoid QA, this paper aims to develop a dataset for training Japanese how-to tip QA models. This paper applies one of the state-of-the-art machine comprehension models to the Japanese how-to tip QA dataset. The trained how-to tip QA model is also compared with a factoid QA model trained with a Japanese factoid QA dataset. Evaluation results revealed that the how-to tip machine comprehension performance was almost comparative with that of the factoid machine comprehension even with the training data size reduced to around 4{\%} of the factoid machine comprehension. Thus, the how-to tip machine comprehension task requires much less training data compared with the factoid machine comprehension task.
Maintaining Quality in FEVER Annotation
Authors: Leon Derczynski, Julie Binau, Henri Schulte
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7

We propose two measures for measuring the quality of constructed claims in the FEVER task. Annotating data for this task involves the creation of supporting and refuting claims over a set of evidence. Automatic annotation processes often leave superficial patterns in data, which learning systems can detect instead of performing the underlying task. Humans also can leave these superficial patterns, either voluntarily or involuntarily (due to e.g. fatigue). The two measures introduced attempt to detect the impact of these superficial patterns. One is a new information-theoretic and distributionality based measure, \textit{DCI}; and the other an extension of neural probing work over the ARCT task, \textit{utility}. We demonstrate these measures over a recent major dataset, that from the English FEVER task in 2019.
Distilling the Evidence to Augment Fact Verification Models
Authors: Beatrice Portelli, Jason Zhao, Tal Schuster, Giuseppe Serra, Enrico Santus
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Fact Verification

The alarming spread of fake news in social media, together with the impossibility of scaling manual fact verification, motivated the development of natural language processing techniques to automatically verify the veracity of claims. Most approaches perform a claim-evidence classification without providing any insights about why the claim is trustworthy or not. We propose, instead, a model-agnostic framework that consists of two modules: (1) a span extractor, which identifies the crucial information connecting claim and evidence; and (2) a classifier that combines claim, evidence, and the extracted spans to predict the veracity of the claim. We show that the spans are informative for the classifier, improving performance and robustness. Tested on several state-of-the-art models over the Fever dataset, the enhanced classifiers consistently achieve higher accuracy while also showing reduced sensitivity to artifacts in the claims.
A Report on the 2020 VUA and TOEFL Metaphor Detection Shared Task
Authors: Chee Wee (Ben) Leong, Beata Beigman Klebanov, Chris Hamill, Egon Stemle, Rutuja Ubale, Xianyang Chen
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Language Identification, Native Language Identification

In this paper, we report on the shared task on metaphor identification on VU Amsterdam Metaphor Corpus and on a subset of the TOEFL Native Language Identification Corpus. The shared task was conducted as apart of the ACL 2020 Workshop on Processing Figurative Language.
DeepMet: A Reading Comprehension Paradigm for Token-level Metaphor Detection
Authors: Chu Su, ong, Fumiyo Fukumoto, Xiaoxi Huang, Jiyi Li, Rongbo Wang, Zhiqun Chen
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Language Modeling, Language Modelling, POS, Reading Comprehension, Sentence

Machine metaphor understanding is one of the major topics in NLP. Most of the recent attempts consider it as classification or sequence tagging task. However, few types of research introduce the rich linguistic information into the field of computational metaphor by leveraging powerful pre-training language models. We focus a novel reading comprehension paradigm for solving the token-level metaphor detection task which provides an innovative type of solution for this task. We propose an end-to-end deep metaphor detection model named DeepMet based on this paradigm. The proposed approach encodes the global text context (whole sentence), local text context (sentence fragments), and question (query word) information as well as incorporating two types of part-of-speech (POS) features by making use of the advanced pre-training language model. The experimental results by using several metaphor datasets show that our model achieves competitive results in the second shared task on metaphor detection.
Context-Driven Satirical News Generation
Authors: Zachary Horvitz, Nam Do, Michael L. Littman
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Articles, News Generation

While mysterious, humor likely hinges on an interplay of entities, their relationships, and cultural connotations. Motivated by the importance of context in humor, we consider methods for constructing and leveraging contextual representations in generating humorous text. Specifically, we study the capacity of transformer-based architectures to generate funny satirical headlines, and show that both language models and summarization models can be fine-tuned to regularly generate headlines that people find funny. Furthermore, we find that summarization models uniquely support satire-generation by enabling the generation of topical humorous text. Outside of our formal study, we note that headlines generated by our model were accepted via a competitive process into a satirical newspaper, and one headline was ranked as high or better than 73{\%} of human submissions. As part of our work, we contribute a dataset of over 15K satirical headlines paired with ranked contextual information from news articles and Wikipedia.
C-Net: Contextual Network for Sarcasm Detection
Authors: Amit Kumar Jena, Aman Sinha, Rohit Agarwal
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Sarcasm Detection, Sentence

Automatic Sarcasm Detection in conversations is a difficult and tricky task. Classifying an utterance as sarcastic or not in isolation can be futile since most of the time the sarcastic nature of a sentence heavily relies on its context. This paper presents our proposed model, C-Net, which takes contextual information of a sentence in a sequential manner to classify it as sarcastic or non-sarcastic. Our model showcases competitive performance in the Sarcasm Detection shared task organised on CodaLab and achieved 75.0{\%} F1-score on the Twitter dataset and 66.3{\%} F1-score on Reddit dataset.
Applying Transformers and Aspect-based Sentiment Analysis approaches on Sarcasm Detection
Authors: Taha Shangipour ataei, Soroush Javdan, Behrouz Minaei-Bidgoli
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sarcasm Detection, Sentence, Sentiment Analysis

Sarcasm is a type of figurative language broadly adopted in social media and daily conversations. The sarcasm can ultimately alter the meaning of the sentence, which makes the opinion analysis process error-prone. In this paper, we propose to employ bidirectional encoder representations transformers (BERT), and aspect-based sentiment analysis approaches in order to extract the relation between context dialogue sequence and response and determine whether or not the response is sarcastic. The best performing method of ours obtains an F1 score of 0.73 on the Twitter dataset and 0.734 over the Reddit dataset at the second workshop on figurative language processing Shared Task 2020.
Sarcasm Identification and Detection in Conversion Context using BERT
Authors: Kalaivani A., Thenmozhi D.
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Sarcasm Detection

Sarcasm analysis in user conversion text is automatic detection of any irony, insult, hurting, painful, caustic, humour, vulgarity that degrades an individual. It is helpful in the field of sentimental analysis and cyberbullying. As an immense growth of social media, sarcasm analysis helps to avoid insult, hurts and humour to affect someone. In this paper, we present traditional machine learning approaches, deep learning approach (LSTM -RNN) and BERT (Bidirectional Encoder Representations from Transformers) for identifying sarcasm. We have used the approaches to build the model, to identify and categorize how much conversion context or response is needed for sarcasm detection and evaluated on the two social media forums that is twitter conversation dataset and reddit conversion dataset. We compare the performance based on the approaches and obtained the best F1 scores as 0.722, 0.679 for the twitter forums and reddit forums respectively.
Neural Sarcasm Detection using Conversation Context
Authors: Nikhil Jaiswal
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Sarcasm Detection

Social media platforms and discussion forums such as Reddit, Twitter, etc. are filled with figurative languages. Sarcasm is one such category of figurative language whose presence in a conversation makes language understanding a challenging task. In this paper, we present a deep neural architecture for sarcasm detection. We investigate various pre-trained language representation models (PLRMs) like BERT, RoBERTa, etc. and fine-tune it on the Twitter dataset. We experiment with a variety of PLRMs either on the twitter utterance in isolation or utilizing the contextual information along with the utterance. Our findings indicate that by taking into consideration the previous three most recent utterances, the model is more accurately able to classify a conversation as being sarcastic or not. Our best performing ensemble model achieves an overall F1 score of 0.790, which ranks us second on the leaderboard of the Sarcasm Shared Task 2020.
Context-Aware Sarcasm Detection Using BERT
Authors: Arup Baruah, Kaushik Das, Ferdous Barbhuiya, Kuntal Dey
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Sarcasm Detection

In this paper, we present the results obtained by BERT, BiLSTM and SVM classifiers on the shared task on Sarcasm Detection held as part of The Second Workshop on Figurative Language Processing. The shared task required the use of conversational context to detect sarcasm. We experimented by varying the amount of context used along with the response (response is the text to be classified). The amount of context used includes (i) zero context, (ii) last one, two or three utterances, and (iii) all utterances. It was found that including the last utterance in the dialogue along with the response improved the performance of the classifier for the Twitter data set. On the other hand, the best performance for the Reddit data set was obtained when using only the response without any contextual information. The BERT classifier obtained F-score of 0.743 and 0.658 for the Twitter and Reddit data set respectively.
A Novel Hierarchical BERT Architecture for Sarcasm Detection
Authors: Himani Srivastava, Vaibhav Varshney, Surabhi Kumari, Saurabh Srivastava
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Sarcasm Detection, Sentence

Online discussion platforms are often flooded with opinions from users across the world on a variety of topics. Many such posts, comments, or utterances are often sarcastic in nature, i.e., the actual intent is hidden in the sentence and is different from its literal meaning, making the detection of such utterances challenging without additional context. In this paper, we propose a novel deep learning-based approach to detect whether an utterance is sarcastic or non-sarcastic by utilizing the given contexts ina hierarchical manner. We have used datasets from two online discussion platforms - Twitter and Reddit1for our experiments. Experimental and error analysis shows that the hierarchical models can make full use of history to obtain a better representation of contexts and thus, in turn, can outperform their sequential counterparts.
Detecting Sarcasm in Conversation Context Using Transformer-Based Models
Authors: Adithya Avvaru, Sanath Vobilisetty, Radhika Mamidi
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Sarcasm Detection, Sentence, Sentiment Analysis

Sarcasm detection, regarded as one of the sub-problems of sentiment analysis, is a very typical task because the introduction of sarcastic words can flip the sentiment of the sentence itself. To date, many research works revolve around detecting sarcasm in one single sentence and there is very limited research to detect sarcasm resulting from multiple sentences. Current models used Long Short Term Memory (LSTM) variants with or without attention to detect sarcasm in conversations. We showed that the models using state-of-the-art Bidirectional Encoder Representations from Transformers (BERT), to capture syntactic and semantic information across conversation sentences, performed better than the current models. Based on the data analysis, we estimated that the number of sentences in the conversation that can contribute to the sarcasm and the results agrees to this estimation. We also perform a comparative study of our different versions of BERT-based model with other variants of LSTM model and XLNet (both using the estimated number of conversation sentences) and find out that BERT-based models outperformed them.
Using Conceptual Norms for Metaphor Detection
Authors: Mingyu WAN, Kathleen Ahrens, Emmanuele Chersoni, Menghan Jiang, Qi Su, Rong Xiang, Chu-Ren Huang
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7

This paper reports a linguistically-enriched method of detecting token-level metaphors for the second shared task on Metaphor Detection. We participate in all four phases of competition with both datasets, i.e. Verbs and AllPOS on the VUA and the TOFEL datasets. We use the modality exclusivity and embodiment norms for constructing a conceptual representation of the nodes and the context. Our system obtains an F-score of 0.652 for the VUA Verbs track, which is 5{\%} higher than the strong baselines. The experimental results across models and datasets indicate the salient contribution of using modality exclusivity and modality shift information for predicting metaphoricity.
ALBERT-BiLSTM for Sequential Metaphor Detection
Authors: Shuqun Li, Jingjie Zeng, Jinhui Zhang, Tao Peng, Liang Yang, Hongfei Lin
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: POS

In our daily life, metaphor is a common way of expression. To understand the meaning of a metaphor, we should recognize the metaphor words which play important roles. In the metaphor detection task, we design a sequence labeling model based on ALBERT-LSTM-softmax. By applying this model, we carry out a lot of experiments and compare the experimental results with different processing methods, such as with different input sentences and tokens, or the methods with CRF and softmax. Then, some tricks are adopted to improve the experimental results. Finally, our model achieves a 0.707 F1-score for the all POS subtask and a 0.728 F1-score for the verb subtask on the TOEFL dataset.
Character aware models with similarity learning for metaphor detection
Authors: Tarun Kumar, Yashvardhan Sharma
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Word Embeddings

Recent work on automatic sequential metaphor detection has involved recurrent neural networks initialized with different pre-trained word embeddings and which are sometimes combined with hand engineered features. To capture lexical and orthographic information automatically, in this paper we propose to add character based word representation. Also, to contrast the difference between literal and contextual meaning, we utilize a similarity network. We explore these components via two different architectures - a BiLSTM model and a Transformer Encoder model similar to BERT to perform metaphor identification. We participate in the Second Shared Task on Metaphor Detection on both the VUA and TOFEL datasets with the above models. The experimental results demonstrate the effectiveness of our method as it outperforms all the systems which participated in the previous shared task.