pwc-archive/papers-with-abstracts huggingface dataset:
Dataset Construction for Scientific-Document Writing Support by Extracting Related Work Section and Citations from PDF Papers
Authors: Keita Kobayashi, Kohei Koyama, Hiromi Narimatsu, Yasuhiro Minami
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

To augment datasets used for scientific-document writing support research, we extract texts from “Related Work” sections and citation information in PDF-formatted papers published in English. The previous dataset was constructed entirely with Tex-formatted papers, from which it is easy to extract citation information. However, since many publicly available papers in various fields are provided only in PDF format, a dataset constructed using only Tex papers has limited utility. To resolve this problem, we augment the existing dataset by extracting the titles of sections using the visual features of PDF documents and extracting the Related Work section text using the explicit title information. Since text generated from the figures and footnotes appearing in the extraction target areas is considered noise, we remove instances of such text. Moreover, we map the cited paper’s information obtained using existing tools to citation marks detected by regular expression rules, resulting in pairs of cited paper information and text of the Related Work section. By evaluating body text extraction and citation mapping in the constructed dataset, the accuracy of the proposed dataset was found to be close to that of the previous dataset. Accordingly, we demonstrated the possibility of building a significantly augmented dataset.
RuPAWS: A Russian Adversarial Dataset for Paraphrase Identification
Authors: Nikita Martynov, Irina Krotova, Varvara Logacheva, Alexander Panchenko, Olga Kozlova, Nikita Semenov
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Paraphrase Identification

Paraphrase identification task can be easily challenged by changing word order, e.g. as in “Can a good person become bad?”. While for English this problem was tackled by the PAWS dataset (Zhang et al., 2019), datasets for Russian paraphrase detection lack non-paraphrase examples with high lexical overlap. We present RuPAWS, the first adversarial dataset for Russian paraphrase identification. Our dataset consists of examples from PAWS translated to the Russian language and manually annotated by native speakers. We compare it to the largest available dataset for Russian ParaPhraser and show that the best available paraphrase identifiers for the Russian language fail on the RuPAWS dataset. At the same time, the state-of-the-art paraphrasing model RuBERT trained on both RuPAWS and ParaPhraser obtains high performance on the RuPAWS dataset while maintaining its accuracy on the ParaPhraser benchmark. We also show that RuPAWS can measure the sensitivity of models to word order and syntax structure since simple baselines fail even when given RuPAWS training samples.
Atril: an XML Visualization System for Corpus Texts
Authors: Andressa Rodrigues Gomide, Conceição Carapinha, Cornelia Plag
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Management, Word Alignment

This paper presents Atril, an XML visualization system for corpus texts, developed for, but not restricted to, the project Corpus de Audiências (CorAuDis), a corpus composed of transcripts of sessions of criminal proceedings recorded at the Coimbra Court. The main aim of the tool is to provide researchers with a web-based environment that allows for an easily customizable visualization of corpus texts with heavy structural annotation. Existing corpus analysis tools such as SketchEngine, TEITOK and CQPweb offer some kind of visualization mechanisms, but, to our knowledge, none meets our project’s main needs. Our requirements are a system that is open-source; that can be easily connected to CQPweb and TEITOK, that provides a full text-view with switchable visualization templates, that allows for the visualization of overlapping utterances. To meet those requirements, we created Atril, a module with a corpus XML file viewer, a visualization management system, and a word alignment tool.
Universal Dependencies for Punjabi
Authors: Aryaman Arora
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Diversity, POS

We introduce the first Universal Dependencies treebank for Punjabi (written in the Gurmukhi script) and discuss corpus design and linguistic phenomena encountered in annotation. The treebank covers a variety of genres and has been annotated for POS tags, dependency relations, and graph-based Enhanced Dependencies. We aim to expand the diversity of coverage of Indo-Aryan languages in UD.
TeSum: Human-Generated Abstractive Summarization Corpus for Telugu
Authors: Ashok Urlana, Nirmal Surange, Pavan Baswani, Priyanka Ravva, Manish Shrivastava
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Abstractive Text Summarization

Expert human annotation for summarization is definitely an expensive task, and can not be done on huge scales. But with this work, we show that even with a crowd sourced summary generation approach, quality can be controlled by aggressive expert informed filtering and sampling-based human evaluation. We propose a pipeline that crowd-sources summarization data and then aggressively filters the content via: automatic and partial expert evaluation. Using this pipeline we create a high-quality Telugu Abstractive Summarization dataset (TeSum) which we validate with sampling-based human evaluation. We also provide baseline numbers for various models commonly used for summarization. A number of recently released datasets for summarization, scraped the web-content relying on the assumption that summary is made available with the article by the publishers. While this assumption holds for multiple resources (or news-sites) in English, it should not be generalised across languages without thorough analysis and verification. Our analysis clearly shows that this assumption does not hold true for most Indian language news resources. We show that our proposed filtration pipeline can even be applied to these large-scale scraped datasets to extract better quality article-summary pairs.
A Corpus of Simulated Counselling Sessions with Dialog Act Annotation
Authors: John Lee, Haley Fong, Lai Shuen Judy Wong, Chun Chung Mak, Chi Hin Yip, Ching Wah Larry Ng
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

We present a corpus of simulated counselling sessions consisting of speech- and text-based dialogs in Cantonese. Consisting of 152K Chinese characters, the corpus labels the dialog act of both client and counsellor utterances, segments each dialog into stages, and identifies the forward and backward links in the dialog. We analyze the distribution of client and counsellor communicative intentions in the various stages, and discuss significant patterns of the dialog flow.
HADREB: Human Appraisals and (English) Descriptions of Robot Emotional Behaviors
Authors: Josue Torres-Fonsesca, Casey Kennington
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Attribute, Language Modelling

Humans sometimes anthropomorphize everyday objects, but especially robots that have human-like qualities and that are often able to interact with and respond to humans in ways that other objects cannot. Humans especially attribute emotion to robot behaviors, partly because humans often use and interpret emotions when interacting with other humans, and they apply that capability when interacting with robots. Moreover, emotions are a fundamental part of the human language system and emotions are used as scaffolding for language learning, making them an integral part of language learning and meaning. However, there are very few datasets that explore how humans perceive the emotional states of robots and how emotional behaviors relate to human language. To address this gap we have collected HADREB, a dataset of human appraisals and English descriptions of robot emotional behaviors collected from over 30 participants. These descriptions and human emotion appraisals are collected using the Mistyrobotics Misty II and the Digital Dream Labs Cozmo (formerly Anki) robots. The dataset contains English descriptions and emotion appraisals of more than 500 descriptions and graded valence labels of 8 emotion pairs for each behavior and each robot. In this paper we describe the process of collecting and cleaning the data, give a general analysis of the data, and evaluate the usefulness of the dataset in two experiments, one using a language model to map descriptions to emotions, the other maps robot behaviors to emotions.
Dialogue Collection for Recording the Process of Building Common Ground in a Collaborative Task
Authors: Koh Mitsuda, Ryuichiro Higashinaka, Yuhei Oga, Sen Yoshida
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

To develop a dialogue system that can build common ground with users, the process of building common ground through dialogue needs to be clarified. However, the studies on the process of building common ground have not been well conducted; much work has focused on finding the relationship between a dialogue in which users perform a collaborative task and its task performance represented by the final result of the task. In this study, to clarify the process of building common ground, we propose a data collection method for automatically recording the process of building common ground through a dialogue by using the intermediate result of a task. We collected 984 dialogues, and as a result of investigating the process of building common ground, we found that the process can be classified into several typical patterns and that conveying each worker’s understanding through affirmation of a counterpart’s utterances especially contributes to building common ground. In addition, toward dialogue systems that can build common ground, we conducted an automatic estimation of the degree of built common ground and found that its degree can be estimated quite accurately.
Collection and Analysis of Travel Agency Task Dialogues with Age-Diverse Speakers
Authors: Michimasa Inaba, Yuya Chiba, Ryuichiro Higashinaka, Kazunori Komatani, Yusuke Miyao, Takayuki Nagai
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

When individuals communicate with each other, they use different vocabulary, speaking speed, facial expressions, and body language depending on the people they talk to. This paper focuses on the speaker’s age as a factor that affects the change in communication. We collected a multimodal dialogue corpus with a wide range of speaker ages. As a dialogue task, we focus on travel, which interests people of all ages, and we set up a task based on a tourism consultation between an operator and a customer at a travel agency. This paper provides details of the dialogue task, the collection procedure and annotations, and the analysis on the characteristics of the dialogues and facial expressions focusing on the age of the speakers. Results of the analysis suggest that the adult speakers have more independent opinions, the older speakers more frequently express their opinions frequently compared with other age groups, and the operators expressed a smile more frequently to the minor speakers.
Strategy-level Entrainment of Dialogue System Users in a Creative Visual Reference Resolution Task
Authors: Deepthi Karkada, Ramesh Manuvinakurike, Maike Paetzel-Prüsmann, Kallirroi Georgila
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Descriptive, Dialogue Management, Knowledge Graphs, Management, Natural Language Understanding

In this work, we study entrainment of users playing a creative reference resolution game with an autonomous dialogue system. The language understanding module in our dialogue system leverages annotated human-wizard conversational data, openly available knowledge graphs, and crowd-augmented data. Unlike previous entrainment work, our dialogue system does not attempt to make the human conversation partner adopt lexical items in their dialogue, but rather to adapt their descriptive strategy to one that is simpler to parse for our natural language understanding unit. By deploying this dialogue system through a crowd-sourced study, we show that users indeed entrain on a “strategy-level” without the change of strategy impinging on their creativity. Our work thus presents a promising future research direction for developing dialogue management systems that can strategically influence people’s descriptive strategy to ease the system’s language understanding in creative tasks.
E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service
Authors: Meihuizi Jia, Ruixue Liu, Peiying Wang, Yang song, Zexi Xi, Haobin Li, Xin Shen, Meng Chen, Jinhui Pang, Xiaodong He
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Conversational Recommendation, Dialogue Management, Management

There has been a growing interest in developing conversational recommendation system (CRS), which provides valuable recommendations to users through conversations. Compared to the traditional recommendation, it advocates wealthier interactions and provides possibilities to obtain users’ exact preferences explicitly. Nevertheless, the corresponding research on this topic is limited due to the lack of broad-coverage dialogue corpus, especially real-world dialogue corpus. To handle this issue and facilitate our exploration, we construct E-ConvRec, an authentic Chinese dialogue dataset consisting of over 25k dialogues and 770k utterances, which contains user profile, product knowledge base (KB), and multiple sequential real conversations between users and recommenders. Next, we explore conversational recommendation in a real scene from multiple facets based on the dataset. Therefore, we particularly design three tasks: user preference recognition, dialogue management, and personalized recommendation. In the light of the three tasks, we establish baseline results on E-ConvRec to facilitate future studies.
SHONGLAP: A Large Bengali Open-Domain Dialogue Corpus
Authors: Syed Mostofa Monsur, Sakib Chowdhury, Md Shahrar Fatemi, Shafayat Ahmed
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

We introduce SHONGLAP, a large annotated open-domain dialogue corpus in Bengali language. Due to unavailability of high-quality dialogue datasets for low-resource languages like Bengali, existing neural open-domain dialogue systems suffer from data scarcity. We propose a framework to prepare large-scale open-domain dialogue datasets from publicly available multi-party discussion podcasts, talk-shows and label them based on weak-supervision techniques which is particularly suitable for low-resource settings. Using this framework, we prepared our corpus, the first reported Bengali open-domain dialogue corpus (7.7k+ fully annotated dialogues in total) which can serve as a strong baseline for future works. Experimental results show that our corpus improves performance of large language models (BanglaBERT) in case of downstream classification tasks during fine-tuning.
A Comparison of Praising Skills in Face-to-Face and Remote Dialogues
Authors: Toshiki Onishi, Asahi Ogushi, Yohei Tahara, Ryo Ishii, Atsushi Fukayama, Takao Nakamura, Akihiro Miyata
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

Praising behavior is considered to an important method of communication in daily life and social activities. An engineering analysis of praising behavior is therefore valuable. However, a dialogue corpus for this analysis has not yet been developed. Therefore, we develop corpuses for face-to-face and remote two-party dialogues with ratings of praising skills. The corpuses enable us to clarify how to use verbal and nonverbal behaviors for successfully praise. In this paper, we analyze the differences between the face-to-face and remote corpuses, in particular the expressions in adjudged praising scenes in both corpuses, and also evaluated praising skills. We also compare differences in head motion, gaze behavior, facial expression in high-rated praising scenes in both corpuses. The results showed that the distribution of praising scores was similar in face-to-face and remote dialogues, although the ratio of the number of praising scenes to the number of utterances was different. In addition, we confirmed differences in praising behavior in face-to-face and remote dialogues.
Comparing Approaches to Language Understanding for Human-Robot Dialogue: An Error Taxonomy and Analysis
Authors: Ada Tur, David Traum
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

In this paper, we compare two different approaches to language understanding for a human-robot interaction domain in which a human commander gives navigation instructions to a robot. We contrast a relevance-based classifier with a GPT-2 model, using about 2000 input-output examples as training data. With this level of training data, the relevance-based model outperforms the GPT-2 based model 79% to 8%. We also present a taxonomy of types of errors made by each model, indicating that they have somewhat different strengths and weaknesses, so we also examine the potential for a combined model.
SPORTSINTERVIEW: A Large-Scale Sports Interview Benchmark for Entity-centric Dialogues
Authors: Hanfei Sun, Ziyuan Cao, Diyi Yang
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

We propose a novel knowledge grounded dialogue (interview) dataset SPORTSINTERVIEW set in the domain of sports interview. Our dataset contains two types of external knowledge sources as knowledge grounding, and is rich in content, containing about 150K interview sessions and 34K distinct interviewees. Compared to existing knowledge grounded dialogue datasets, our interview dataset is larger in size, comprises natural dialogues revolving around real-world sports matches, and have more than one dimension of external knowledge linking. We performed several experiments on SPORTSINTERVIEW and found that models such as BART fine-tuned on our dataset are able to learn lots of relevant domain knowledge and generate meaningful sentences (questions or responses). However, their performance is still far from humans (by comparing to gold sentences in the dataset) and hence encourages future research utilizing SPORTSINTERVIEW.
Who’s in, who’s out? Predicting the Inclusiveness or Exclusiveness of Personal Pronouns in Parliamentary Debates
Authors: Ines Rehbein, Josef Ruppenhofer
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

This paper presents a compositional annotation scheme to capture the clusivity properties of personal pronouns in context, that is their ability to construct and manage in-groups and out-groups by including/excluding the audience and/or non-speech act participants in reference to groups that also include the speaker. We apply and test our schema on pronoun instances in speeches taken from the German parliament. The speeches cover a time period from 2017-2021 and comprise manual annotations for 3,126 sentences. We achieve high inter-annotator agreement for our new schema, with a Cohen’s κ in the range of 89.7-93.2 and a percentage agreement of > 96%. Our exploratory analysis of in/exclusive pronoun use in the parliamentary setting provides some face validity for our new schema. Finally, we present baseline experiments for automatically predicting clusivity in political debates, with promising results for many referential constellations, yielding an overall 84.9% micro F1 for all pronouns.
A Language Modelling Approach to Quality Assessment of OCR’ed Historical Text
Authors: Callum Booth, Robert Shoemaker, Robert Gaizauskas
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Language Modeling, Language Modelling, Optical Character Recognition (OCR)

We hypothesise and evaluate a language model-based approach for scoring the quality of OCR transcriptions in the British Library Newspapers (BLN) corpus parts 1 and 2, to identify the best quality OCR for use in further natural language processing tasks, with a wider view to link individual newspaper reports of crime in nineteenth-century London to the Digital Panopticon—a structured repository of criminal lives. We mitigate the absence of gold standard transcriptions of the BLN corpus by utilising a corpus of genre-adjacent texts that capture the common and legal parlance of nineteenth-century London—the Proceedings of the Old Bailey Online—with a view to rank the BLN transcriptions by their OCR quality.
Identifying Copied Fragments in a 18th Century Dutch Chronicle
Authors: Roser Morante, Eleanor L. T. Smith, Lianne Wilhelmus, Alie Lassche, Erika Kuijpers
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Authorship Verification, Clustering

We apply computational stylometric techniques to an 18th century Dutch chronicle to determine which fragments of the manuscript represent the author’s own original work and which show signs of external source use through either direct copying or paraphrasing. Through stylometric methods the majority of text fragments in the chronicle can be correctly labelled as either the author’s own words, direct copies from sources or paraphrasing. Our results show that clustering text fragments based on stylometric measures is an effective methodology for authorship verification of this document; however, this approach is less effective when personal writing style is masked by author independent styles or when applied to paraphrased text.
A Study of Distant Viewing of ukiyo-e prints
Authors: Konstantina Liagkou, John Pavlopoulos, Ewa Machotka
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

This paper contributes to studying relationships between Japanese topography and places featured in early modern landscape prints, so-called ukiyo-e or ‘pictures of the floating world’. The printed inscriptions on these images feature diverse place-names, both man-made and natural formations. However, due to the corpus’s richness and diversity, the precise nature of artistic mediation of the depicted places remains little understood. In this paper, we explored a new analytical approach based on the macroanalysis of images facilitated by Natural Language Processing technologies. This paper presents a small dataset with inscriptions on prints that have been annotated by an art historian for included place-name entities. Our dataset is released for public use. By fine-tuning and applying a Japanese BERT-based Name Entity Recogniser, we provide a use-case of a macroanalysis of a visual dataset that is hosted by the digital database of the Art Research Center at the Ritsumeikan University, Kyoto. Our work studies the relationship between topography and its visual renderings in early modern Japanese ukiyo-e landscape prints, demonstrating how an art historian’s work can be improved with Natural Language Processing toward distant viewing of visual datasets. We release our dataset and code for public use: https://github.com/connalia/ukiyo-e_meisho_nlp
CCTAA: A Reproducible Corpus for Chinese Authorship Attribution Research
Authors: Haining Wang, Allen Riddell
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Authorship Attribution

Authorship attribution infers the likely author of an unsigned, single-authored document from a pool of candidates. Despite recent advances, a lack of standard, reproducible testbeds for Chinese language documents impedes progress. In this paper, we present the Chinese Cross-Topic Authorship Attribution (CCTAA) corpus. It is the first standard testbed for authorship attribution on contemporary Chinese prose. The cross-topic design and relatively inflexible genre of newswire contribute to an appropriate level of difficulty. It supports reproducible research by using pre-defined data splits. We show that a sequence classifier based on pre-trained Chinese RoBERTa embedding and a support vector machine classifier using function character n-gram frequency features perform below expectations on this task. The code for generating the corpus and reproducing the baselines is freely available at https://codeberg.org/haining/cctaa.