pwc-archive/papers-with-abstracts huggingface dataset:
Boundary Detection and Categorization of Argument Aspects via Supervised Learning
Authors: Mattes Ruckdeschel, Gregor Wiedemann
Date: 2022-10-01 00:00:00
Proceeding: ArgMining (ACL) 2022 10
Tasks: Argument Mining, Boundary Detection, Sentence

Aspect-based argument mining (ABAM) is the task of automatic _detection_ and _categorization_ of argument aspects, i.e. the parts of an argumentative text that contain the issue-specific key rationale for its conclusion. From empirical data, overlapping but not congruent sets of aspect categories can be derived for different topics. So far, two supervised approaches to detect aspect boundaries, and a smaller number of unsupervised clustering approaches to categorize groups of similar aspects have been proposed. With this paper, we introduce the Argument Aspect Corpus (AAC) that contains token-level annotations of aspects in 3,547 argumentative sentences from three highly debated topics. This dataset enables both the supervised learning of boundaries and categorization of argument aspects. During the design of our annotation process, we noticed that it is not clear from the outset at which contextual unit aspects should be coded. We, thus, experiment with classification at the token, chunk, and sentence level granularity. Our finding is that the chunk level provides the most useful information for applications. At the same time, it produces the best performing results in our tested supervised learning setups.
Predicting the Presence of Reasoning Markers in Argumentative Text
Authors: Jonathan Clayton, Rob Gaizauskas
Date: 2022-10-01 00:00:00
Proceeding: ArgMining (ACL) 2022 10
Tasks: Argument Mining

This paper proposes a novel task in Argument Mining, which we will refer to as Reasoning Marker Prediction. We reuse the popular Persuasive Essays Corpus (Stab and Gurevych, 2014). Instead of using this corpus for Argument Structure Parsing, we use a simple heuristic method to identify text spans which we can identify as reasoning markers. We propose baseline methods for predicting the presence of these reasoning markers automatically, and make a script to generate the data for the task publicly available.
Detecting Arguments in CJEU Decisions on Fiscal State Aid
Authors: Giulia Grundler, Piera Santin, Andrea Galassi, Federico Galli, Francesco Godano, Francesca Lagioia, Elena Palmieri, Federico Ruggeri, Giovanni Sartor, Paolo Torroni
Date: 2022-10-01 00:00:00
Proceeding: ArgMining (ACL) 2022 10
Tasks: Argument Mining

The successful application of argument mining in the legal domain can dramatically impact many disciplines related to law. For this purpose, we present Demosthenes, a novel corpus for argument mining in legal documents, composed of 40 decisions of the Court of Justice of the European Union on matters of fiscal state aid. The annotation specifies three hierarchical levels of information: the argumentative elements, their types, and their argument schemes. In our experimental evaluation, we address 4 different classification tasks, combining advanced language models and traditional classifiers.
Multimodal Argument Mining: A Case Study in Political Debates
Authors: Eleonora Mancini, Federico Ruggeri, Andrea Galassi, Paolo Torroni
Date: 2022-10-01 00:00:00
Proceeding: ArgMining (ACL) 2022 10
Tasks: Argument Mining

We propose a study on multimodal argument mining in the domain of political debates. We collate and extend existing corpora and provide an initial empirical study on multimodal architectures, with a special emphasis on input encoding methods. Our results provide interesting indications about future directions in this important domain.
A Robustness Evaluation Framework for Argument Mining
Authors: Mehmet Sofi, Matteo Fortier, Oana Cocarascu
Date: 2022-10-01 00:00:00
Proceeding: ArgMining (ACL) 2022 10
Tasks: Argument Mining, Sentence

Standard practice for evaluating the performance of machine learning models for argument mining is to report different metrics such as accuracy or F1. However, little is usually known about the model’s stability and consistency when deployed in real-world settings. In this paper, we propose a robustness evaluation framework to guide the design of rigorous argument mining models. As part of the framework, we introduce several novel robustness tests tailored specifically to argument mining tasks. Additionally, we integrate existing robustness tests designed for other natural language processing tasks and re-purpose them for argument mining. Finally, we illustrate the utility of our framework on two widely used argument mining corpora, UKP topic-sentences and IBM Debater Evidence Sentence. We argue that our framework should be used in conjunction with standard performance evaluation techniques as a measure of model stability.
On Selecting Training Corpora for Cross-Domain Claim Detection
Authors: Robin Schaefer, René Knaebel, Manfred Stede
Date: 2022-10-01 00:00:00
Proceeding: ArgMining (ACL) 2022 10
Tasks: Argument Mining

Identifying claims in text is a crucial first step in argument mining. In this paper, we investigate factors for the composition of training corpora to improve cross-domain claim detection. To this end, we use four recent argumentation corpora annotated with claims and submit them to several experimental scenarios. Our results indicate that the “ideal” composition of training corpora is characterized by a large corpus size, homogeneous claim proportions, and less formal text domains.
QualiAssistant: Extracting Qualia Structures from Texts
Authors: Manuel Biertz, Lorik Dumani, Markus Nilles, Björn Metzler, Ralf Schenkel
Date: 2022-10-01 00:00:00
Proceeding: ArgMining (ACL) 2022 10
Tasks: Argument Mining, Information Retrieval, POS, Retrieval, TAG

In this paper, we present QualiAssistant, a free and open-source system written in Java for identification and extraction of Qualia structures from any natural language texts having many application scenarios such as argument mining or creating dictionaries. It answers the call for a Qualia bootstrapping tool with a ready-to-use system that can be gradually filled by the community with patterns in multiple languages. Qualia structures express the meaning of lexical items. They describe, e.g., of what kind the item is (formal role), what it includes (constitutive role), how it is brought about (agentive role), and what it is used for (telic role). They are also valuable for various Information Retrieval and NLP tasks. Our application requires search patterns for Qualia structures consisting of POS tag sequences as well as the dataset the user wants to search for Qualias. Samples for both are provided alongside this paper. While samples are in German, QualiAssistant can process all languages for which constituency trees can be generated and patterns are available. Our provided patterns follow a high-precision low-recall design aiming to generate automatic annotations for text mining but can be exchanged easily for other purposes. Our evaluation shows that QualiAssistant is a valuable and reliable tool for finding Qualia structures in unstructured texts.
An Emotion-based Korean Multimodal Empathetic Dialogue System
Authors: Minyoung Jung, Yeongbeom Lim, San Kim, Jin Yea Jang, Saim Shin, Ki-Hoon Lee
Date: 2022-10-01 00:00:00
Proceeding: CAI (COLING) 2022 10

We propose a Korean multimodal dialogue system targeting emotion-based empathetic dialogues because most research in this field has been conducted in a few languages such as English and Japanese and in certain circumstances. Our dialogue system consists of an emotion detector, an empathetic response generator, a monitoring interface, a voice activity detector, a speech recognizer, a speech synthesizer, a gesture classification, and several controllers to provide both multimodality and empathy during a conversation between a human and a machine. For comparisons across visual influence on users, our dialogue system contains two versions of the user interface, a cat face-based user interface and an avatar-based user interface. We evaluated our dialogue system by investigating the dialogues in text and the average mean opinion scores under three different visual conditions, no visual, the cat face-based, and the avatar-based expressions. The experimental results stand for the importance of adequate visual expressions according to user utterances.
BETOLD: A Task-Oriented Dialog Dataset for Breakdown Detection
Authors: Silvia Terragni, Bruna Guedes, Andre Manso, Modestas Filipavicius, Nghia Khau, Roland Mathis
Date: 2022-10-01 00:00:00
Proceeding: CAI (COLING) 2022 10
Tasks: Privacy Preserving

Task-Oriented Dialog (TOD) systems often suffer from dialog breakdowns - situations in which users cannot or do not want to proceed with the conversation. Ideally TOD systems should be able to detect dialog breakdowns to prevent users from quitting a conversation and to encourage them to interact with the system again. In this paper, we present BETOLD, a privacy-preserving dataset for breakdown detection. The dataset consists of user and system turns represented by intents and entity annotations, derived from NLU and NLG dialog manager components. We also propose an attention-based model that detects potential breakdowns using these annotations, instead of the utterances’ text. This approach achieves a comparable performance to the corresponding utterance-only model, while ensuring data privacy.
Insurance Question Answering via Single-turn Dialogue Modeling
Authors: Seon-Ok Na, Young-Min Kim, Seung-Hwan Cho
Date: 2022-10-01 00:00:00
Proceeding: CAI (COLING) 2022 10
Tasks: Intent Detection, Question Answering, slot-filling, Slot Filling, Task-Oriented Dialogue Systems

With great success in single-turn question answering (QA), conversational QA is currently receiving considerable attention. Several studies have been conducted on this topic from different perspectives. However, building a real-world conversational system remains a challenge. This study introduces our ongoing project, which uses Korean QA data to develop a dialogue system in the insurance domain. The goal is to construct a system that provides informative responses to general insurance questions. We present the current results of single-turn QA. A unique aspect of our approach is that we borrow the concepts of intent detection and slot filling from task-oriented dialogue systems. We present details of the data construction process and the experimental results on both learning tasks.
Can We Train a Language Model Inside an End-to-End ASR Model? - Investigating Effective Implicit Language Modeling
Authors: Zhuo Gong, Daisuke Saito, Sheng Li, Hisashi Kawai, Nobuaki Minematsu
Date: 2022-10-01 00:00:00
Proceeding: CAI (COLING) 2022 10
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Language Modeling, Language Modelling, model, speech-recognition, Speech Recognition

Language models (LM) have played crucial roles in automatic speech recognition (ASR) to enhance end-to-end (E2E) ASR systems’ performance. There are two categories of approaches: finding better ways to integrate LMs into ASR systems and adapting on LMs to the task domain. This article will start with a reflection of interpolation-based integration methods of E2E ASR’s scores and LM’s scores. Then we will focus on LM augmentation approaches based on the noisy channel model, which is intrigued by insights obtained from the above reflection. The experiments show that we can enhance an ASR E2E model based on encoder-decoder architecture by pre-training the decoder with text data. This implies the decoder of an E2E model can be treated as an LM and reveals the possibility of enhancing the E2E model without an external LM. Based on those ideas, we proposed the implicit language model canceling method and then did more discussion about the decoder part of an E2E ASR model. The experimental results on the TED-LIUM2 dataset show that our approach achieves a 3.4% relative WER reduction compared with the baseline system, and more analytic experiments provide concrete experimental supports for our assumption.
Semantic Content Prediction for Generating Interviewing Dialogues to Elicit Users’ Food Preferences
Authors: Jie Zeng, Tatsuya Sakato, Yukiko Nakano
Date: 2022-10-01 00:00:00
Proceeding: CAI (COLING) 2022 10
Tasks: Dialogue Generation, Retrieval, Sentence

Dialogue systems that aim to acquire user models through interactions with users need to have interviewing functionality. In this study, we propose a method to generate interview dialogues to build a dialogue system that acquires user preferences for food. First, we collected 118 text-based dialogues between the interviewer and customer and annotated the communicative function and semantic content of the utterances. Next, using the corpus as training data, we created a classification model for the communicative function of the interviewer’s next utterance and a generative model that predicts the semantic content of the utterance based on the dialogue history. By representing semantic content as a sequence of tokens, we evaluated the semantic content prediction model using BLEU. The results demonstrated that the semantic content produced by the proposed method was closer to the ground truth than the semantic content transformed from the output text generated by the retrieval model and GPT-2. Further, we present some examples of dialogue generation by applying model outputs to template-based sentence generation.
Learning to Evaluate Humor in Memes Based on the Incongruity Theory
Authors: Kohtaro Tanaka, Hiroaki Yamane, Yusuke Mori, Yusuke Mukuta, Tatsuya Harada
Date: 2022-10-01 00:00:00
Proceeding: CAI (COLING) 2022 10
Tasks: Binary Classification

Memes are a widely used means of communication on social media platforms, and are known for their ability to “go viral”. In prior works, researchers have aimed to develop an AI system to understand humor in memes. However, existing methods are limited by the reliability and consistency of the annotations in the dataset used to train the underlying models. Moreover, they do not explicitly take advantage of the incongruity between images and their captions, which is known to be an important element of humor in memes. In this study, we first gathered real-valued humor annotations of 7,500 memes through a crowdwork platform. Based on this data, we propose a refinement process to extract memes that are not influenced by interpersonal differences in the perception of humor and a method designed to extract and utilize incongruities between images and captions. The results of an experimental comparison with models using vision and language pretraining models show that our proposed approach outperformed other models in a binary classification task of evaluating whether a given meme was humorous.
Focus on FoCus: Is FoCus focused on Context, Knowledge and Persona?
Authors: SeungYoon Lee, Jungseob Lee, Chanjun Park, Sugyeong Eo, Hyeonseok Moon, Jaehyung Seo, Jeongbae Park, Heuiseok Lim
Date: 2022-10-01 00:00:00
Proceeding: CCGPK (COLING) 2022 10
Tasks: Dialogue Generation, Question Answering

Rather than continuing the conversation based on personalized or implicit information, the existing conversation system generates dialogue by focusing only on the superficial content. To solve this problem, FoCus was recently released. FoCus is a persona-knowledge grounded dialogue generation dataset that leverages Wikipedia’s knowledge and personal persona, focusing on the landmarks provided by Google, enabling user-centered conversation. However, a closer empirical study is needed since research in the field is still in its early stages. Therefore, we fling two research questions about FoCus. “Is the FoCus whether for conversation or question answering?” to identify the structural problems of the dataset. “Does the FoCus model do real knowledge blending?” to closely demonstrate that the model acquires actual knowledge. As a result of the experiment, we present that the FoCus model could not correctly blend the knowledge according to the input dialogue and that the dataset design is unsuitable for the multi-turn conversation.
Proto-Gen: An end-to-end neural generator for persona and knowledge grounded response generation
Authors: Sougata Saha, Souvik Das, Rohini Srihari
Date: 2022-10-01 00:00:00
Proceeding: CCGPK (COLING) 2022 10
Tasks: Decoder, Fact Selection, Response Generation

In this paper we detail the implementation of Proto-Gen, an end-to-end neural response generator capable of selecting appropriate persona and fact sentences from available options, and generating persona and fact grounded responses. Incorporating a novel interaction layer in an encoder-decoder architecture, Proto-Gen facilitates learning dependencies between facts, persona and the context, and outperforms existing baselines on the FoCus dataset for both the sub-tasks of persona and fact selection, and response generation. We further fine tune Proto-Gen’s hyperparameters, and share our results and findings.
PERSONACHATGEN: Generating Personalized Dialogues using GPT-3
Authors: Young-Jun Lee, Chae-Gyun Lim, Yunsu Choi, Ji-Hui Lm, Ho-Jin Choi
Date: 2022-10-01 00:00:00
Proceeding: CCGPK (COLING) 2022 10
Tasks: Sentence

Recently, many prior works have made their own agents generate more personalized and engaging responses using personachat. However, since this dataset is frozen in 2018, the dialogue agents trained on this dataset would not know how to interact with a human who loves “Wandavision.” One way to alleviate this problem is to create a large-scale dataset. In this work, we introduce the pipeline of creating personachatgen, which is comprised of three main components: Creating (1) profilegen, (2) Persona Set, and (3) personachatgen. To encourage GPT-3’s generation ability, we also defined a taxonomy of hierarchical persona category derived from social profiling taxonomy. To create the speaker consistent persona set, we propose a simple contradiction-based iterative sentence replacement algorithm, named CoNL. Moreover, to prevent GPT-3 generating harmful content, we presented two filtering pipelines, one each for profilegen and personachatgen. Through analyzing of personachatgen, we showed that GPT-3 can generate personalized dialogue containing diverse persona. Furthermore, we revealed a state-of-the-art Blender 90M trained on our dataset that leads to higher performance.
KOJAK: A New Corpus for Studying German Discourse Particle ja
Authors: Adil Soubki, Owen Rambow, Chong Kang
Date: 2022-10-01 00:00:00
Proceeding: COLING (CODI, CRAC) 2022 10

In German, ja can be used as a discourse particle to indicate that a proposition, according to the speaker, is believed by both the speaker and audience. We use this observation to create KoJaK, a distantly-labeled English dataset derived from Europarl for studying when a speaker believes a statement to be common ground. This corpus is then analyzed to identify lexical choices in English that correspond with German ja. Finally, we perform experiments on the dataset to predict if an English clause corresponds to a German clause containing ja and achieve an F-measure of 75.3% on a balanced test corpus.
Evaluating How Users Game and Display Conversation with Human-Like Agents
Authors: Won Ik Cho, SooMin Kim, Eujeong Choi, Younghoon Jeong
Date: 2022-10-01 00:00:00
Proceeding: COLING (CODI, CRAC) 2022 10
Tasks: Chatbot

Recently, with the advent of high-performance generative language models, artificial agents that communicate directly with the users have become more human-like. This development allows users to perform a diverse range of trials with the agents, and the responses are sometimes displayed online by users who share or show-off their experiences. In this study, we explore dialogues with a social chatbot uploaded to an online community, with the aim of understanding how users game human-like agents and display their conversations. Having done this, we assert that user postings can be investigated from two aspects, namely conversation topic and purpose of testing, and suggest a categorization scheme for the analysis. We analyze 639 dialogues to develop an annotation protocol for the evaluation, and measure the agreement to demonstrate the validity. We find that the dialogue content does not necessarily reflect the purpose of testing, and also that users come up with creative strategies to game the agent without being penalized.
Easy-First Bottom-Up Discourse Parsing via Sequence Labelling
Authors: Andrew Shen, Fajri Koto, Jey Han Lau, Timothy Baldwin
Date: 2022-10-01 00:00:00
Proceeding: COLING (CODI, CRAC) 2022 10
Tasks: Discourse Parsing

We propose a novel unconstrained bottom-up approach for rhetorical discourse parsing based on sequence labelling of adjacent pairs of discourse units (DUs), based on the framework of Koto et al. (2021). We describe the unique training requirements of an unconstrained parser, and explore two different training procedures: (1) fixed left-to-right; and (2) random order in tree construction. Additionally, we introduce a novel dynamic oracle for unconstrained bottom-up parsing. Our proposed parser achieves competitive results for bottom-up rhetorical discourse parsing.
Using Translation Process Data to Explore Explicitation and Implicitation through Discourse Connectives
Authors: Ekaterina Lapshinova-Koltunski, Michael Carl
Date: 2022-10-01 00:00:00
Proceeding: COLING (CODI, CRAC) 2022 10
Tasks: Translation

We look into English-German translation process data to analyse explicitation and implicitation phenomena of discourse connectives. For this, we use the database CRITT TPR-DB which contains translation process data with various features that elicit online translation behaviour. We explore the English-German part of the data for discourse connectives that are either omitted or inserted in the target, as well as cases when changing a weak signal to strong one, or the other way around. We determine several features that have an impact on cognitive effort during translation for explicitation and implicitation. Our results show that cognitive load caused by implicitation and explicitation may depend on the discourse connectives used, as well as on the strength and the type of the relations the connectives convey.
Label distributions help implicit discourse relation classification
Authors: Frances Yung, Kaveri Anuranjana, Merel Scholman, Vera Demberg
Date: 2022-10-01 00:00:00
Proceeding: COLING (CODI, CRAC) 2022 10
Tasks: Classification, Implicit Discourse Relation Classification, Relation, Relation Classification

Implicit discourse relations can convey more than one relation sense, but much of the research on discourse relations has focused on single relation senses. Recently, DiscoGeM, a novel multi-domain corpus, which contains 10 crowd-sourced labels per relational instance, has become available. In this paper, we analyse the co-occurrences of relations in DiscoGem and show that they are systematic and characteristic of text genre. We then test whether information on multi-label distributions in the data can help implicit relation classifiers. Our results show that incorporating multiple labels in parser training can improve its performance, and yield label distributions which are more similar to human label distributions, compared to a parser that is trained on just a single most frequent label per instance.