pwc-archive/papers-with-abstracts huggingface dataset:
The LiLaH Emotion Lexicon of Croatian, Dutch and Slovene
Authors: Nikola Ljubešić, Ilia Markov, Darja Fišer, Walter Daelemans
Date: 2020-12-01 00:00:00
Proceeding: COLING (PEOPLES) 2020 12
Tasks: Translation

In this paper, we present emotion lexicons of Croatian, Dutch and Slovene, based on manually corrected automatic translations of the English NRC Emotion lexicon. We evaluate the impact of the translation changes by measuring the change in supervised classification results of socially unacceptable utterances when lexicon information is used for feature construction. We further showcase the usage of the lexicons by calculating the difference in emotion distributions in texts containing and not containing socially unacceptable discourse, comparing them across four languages (English, Croatian, Dutch, Slovene) and two topics (migrants and LGBT). We show significant and consistent improvements in automatic classification across all languages and topics, as well as consistent (and expected) emotion distributions across all languages and topics, proving for the manually corrected lexicons to be a useful addition to the severely lacking area of emotion lexicons, the crucial resource for emotive analysis of text.
Semantic Guidance of Dialogue Generation with Reinforcement Learning
Authors: Cheng-Hsun Hsueh, Wei-Yun Ma
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7
Tasks: Decoder, Dialogue Generation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Neural encoder-decoder models have shown promising performance for human-computer dialogue systems over the past few years. However, due to the maximum-likelihood objective for the decoder, the generated responses are often universal and safe to the point that they lack meaningful information and are no longer relevant to the post. To address this, in this paper, we propose semantic guidance using reinforcement learning to ensure that the generated responses indeed include the given or predicted semantics and that these semantics do not appear repeatedly in the response. Synsets, which comprise sets of manually defined synonyms, are used as the form of assigned semantics. For a given/assigned/predicted synset, only one of its synonyms should appear in the generated response; this constitutes a simple but effective semantic-control mechanism. We conduct both quantitative and qualitative evaluations, which show that the generated responses are not only higher-quality but also reflect the assigned semantic controls.
Counseling-Style Reflection Generation Using Generative Pretrained Transformers with Augmented Context
Authors: Siqi Shen, Charles Welch, Rada Mihalcea, Verónica Pérez-Rosas
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7

We introduce a counseling dialogue system that seeks to assist counselors while they are learning and refining their counseling skills. The system generates counselors’reflections – i.e., responses that reflect back on what the client has said given the dialogue history. Our method builds upon the new generative pretrained transformer architecture and enhances it with context augmentation techniques inspired by traditional strategies used during counselor training. Through a set of comparative experiments, we show that the system that incorporates these strategies performs better in the reflection generation task than a system that is just fine-tuned with counseling conversations. To confirm our findings, we present a human evaluation study that shows that our system generates naturally-looking reflections that are also stylistically and grammatically correct.
Conversational Agents for Intelligent Buildings
Authors: Weronika Sieińska, Christian Dondrup, Nancie Gunson, Oliver Lemon
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7

We will demonstrate a deployed conversational AI system that acts as a host of a smart-building on a university campus. The system combines open-domain social conversation with task-based conversation regarding navigation in the building, live resource updates (e.g. available computers) and events in the building. We are able to demonstrate the system on several platforms: Google Home devices, Android phones, and a Furhat robot.
Retico: An incremental framework for spoken dialogue systems
Authors: Thilo Michael
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7
Tasks: Spoken Dialogue Systems, Translation

In this paper we present the newest version of retico - a python-based incremental dialogue framework to create state-of-the-art spoken dialogue systems and simulations. Retico provides a range of incremental modules that are based on services like Google ASR, Google TTS and Rasa NLU. Incremental networks can be created either in code or with a graphical user interface. In this demo we present three use cases that are implemented in retico: a spoken translation tool that translates speech in real-time, a conversation simulation that models turn-taking and a spoken dialogue restaurant information service.
MC-Saar-Instruct: a Platform for Minecraft Instruction Giving Agents
Authors: Arne Köhn, Julia Wichlacz, Christine Schäfer, Álvaro Torralba, Joerg Hoffmann, Alexander Koller
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7
Tasks: Minecraft

We present a comprehensive platform to run human-computer experiments where an agent instructs a human in Minecraft, a 3D blocksworld environment. This platform enables comparisons between different agents by matching users to agents. It performs extensive logging and takes care of all boilerplate, allowing to easily incorporate new agents to evaluate them. Our environment is prepared to evaluate any kind of instruction giving system, recording the interaction and all actions of the user. We provide example architects, a Wizard-of-Oz architect and set-up scripts to automatically download, build and start the platform.
Identifying Collaborative Conversations using Latent Discourse Behaviors
Authors: Ayush Jain, Maria Leonor Pacheco, Steven Lancette, Mahak Goindani, Dan Goldwasser
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7
Tasks: Inductive Bias

In this work, we study collaborative online conversations. Such conversations are rich in content, constructive and motivated by a shared goal. Automatically identifying such conversations requires modeling complex discourse behaviors, which characterize the flow of information, sentiment and community structure within discussions. To help capture these behaviors, we define a hybrid relational model in which relevant discourse behaviors are formulated as discrete latent variables and scored using neural networks. These variables provide the information needed for predicting the overall collaborative characterization of the entire conversational thread. We show that adding inductive bias in the form of latent variables results in performance improvement, while providing a natural way to explain the decision.
A Case Study of User Communication Styles with Customer Service Agents versus Intelligent Virtual Agents
Authors: Timothy Hewitt, Ian Beaver
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7
Tasks: Chatbot, Diversity

We investigate differences in user communication with live chat agents versus a commercial Intelligent Virtual Agent (IVA). This case study compares the two types of interactions in the same domain for the same company filling the same purposes. We compared 16,794 human-to-human conversations and 27,674 conversations with the IVA. Of those IVA conversations, 8,324 escalated to human live chat agents. We then investigated how human-to-human communication strategies change when users first communicate with an IVA in the same conversation thread. We measured quantity, quality, and diversity of language, and analyzed complexity using numerous features. We find that while the complexity of language did not significantly change between modes, the quantity and some quality metrics did vary significantly. This fair comparison provides unique insight into how humans interact with commercial IVAs and how IVA and chatbot designers might better curate training data when automating customer service tasks.
It’s About Time: Turn-Entry Timing For Situated Human-Robot Dialogue
Authors: Felix Gervits, Ravenna Thielstrom, Antonio Roque, Matthias Scheutz
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7
Tasks: Spoken Dialogue Systems

Turn-entry timing is an important requirement for conversation, and one that spoken dialogue systems largely fail at. In this paper, we introduce a computational framework based on work from Psycholinguistics, which is aimed at achieving proper turn-taking timing for situated agents. The approach involves incremental processing and lexical prediction of the turn in progress, which allows a situated dialogue system to start its turn and initiate actions earlier than would otherwise be possible. We evaluate the framework by integrating it within a cognitive robotic architecture and testing performance on a corpus of task-oriented human-robot directives. We demonstrate that: 1) the system is superior to a non-incremental system in terms of faster responses, reduced gap between turns, and the ability to perform actions early, 2) the system can time its turn to come in immediately at a transition point or earlier to produce several types of overlap, and 3) the system is robust to various forms of disfluency in the input. Overall, this domain-independent framework can be integrated into various dialogue systems to improve responsiveness, and is a step toward more natural, human-like turn-taking behavior.
Learning Word Groundings from Humans Facilitated by Robot Emotional Displays
Authors: David McNeill, Casey Kennington
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7

In working towards accomplishing a human-level acquisition and understanding of language, a robot must meet two requirements: the ability to learn words from interactions with its physical environment, and the ability to learn language from people in settings for language use, such as spoken dialogue. In a live interactive study, we test the hypothesis that emotional displays are a viable solution to the cold-start problem of how to communicate without relying on language the robot does not–indeed, cannot–yet know. We explain our modular system that can autonomously learn word groundings through interaction and show through a user study with 21 participants that emotional displays improve the quantity and quality of the inputs provided to the robot.
An Attentive Listening System with Android ERICA: Comparison of Autonomous and WOZ Interactions
Authors: Koji Inoue, Divesh Lala, Kenta Yamamoto, Shizuka Nakamura, Katsuya Takanashi, Tatsuya Kawahara
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7
Tasks: Dialogue Understanding

We describe an attentive listening system for the autonomous android robot ERICA. The proposed system generates several types of listener responses: backchannels, repeats, elaborating questions, assessments, generic sentimental responses, and generic responses. In this paper, we report a subjective experiment with 20 elderly people. First, we evaluated each system utterance excluding backchannels and generic responses, in an offline manner. It was found that most of the system utterances were linguistically appropriate, and they elicited positive reactions from the subjects. Furthermore, 58.2% of the responses were acknowledged as being appropriate listener responses. We also compared the proposed system with a WOZ system where a human operator was operating the robot. From the subjective evaluation, the proposed system achieved comparable scores in basic skills of attentive listening such as encouragement to talk, focused on the talk, and actively listening. It was also found that there is still a gap between the system and the WOZ for more sophisticated skills such as dialogue understanding, showing interest, and empathy towards the user.
rrSDS: Towards a Robot-ready Spoken Dialogue System
Authors: Casey Kennington, Daniele Moro, Lucas Marchand, Jake Carns, David McNeill
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7

Spoken interaction with a physical robot requires a dialogue system that is modular, multimodal, distributive, incremental and temporally aligned. In this demo paper, we make significant contributions towards fulfilling these requirements by expanding upon the ReTiCo incremental framework. We outline the incremental and multimodal modules and how their computation can be distributed. We demonstrate the power and flexibility of our robot-ready spoken dialogue system to be integrated with almost any robot.
Discovering Knowledge Graph Schema from Short Natural Language Text via Dialog
Authors: Subhasis Ghosh, Arpita Kundu, Aniket Pramanick, Indrajit Bhattacharya
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7
Tasks: Active Learning, Knowledge Graphs

We study the problem of schema discovery for knowledge graphs. We propose a solution where an agent engages in multi-turn dialog with an expert for this purpose. Each mini-dialog focuses on a short natural language statement, and looks to elicit the expert’s desired schema-based interpretation of that statement, taking into account possible augmentations to the schema. The overall schema evolves by performing dialog over a collection of such statements. We take into account the probability that the expert does not respond to a query, and model this probability as a function of the complexity of the query. For such mini-dialogs with response uncertainty, we propose a dialog strategy that looks to elicit the schema over as short a dialog as possible. By combining the notion of uncertainty sampling from active learning with generalized binary search, the strategy asks the query with the highest expected reduction of entropy. We show that this significantly reduces dialog complexity while engaging the expert in meaningful dialog.
User Impressions of Questions to Acquire Lexical Knowledge
Authors: Kazunori Komatani, Mikio Nakano
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7

For the acquisition of knowledge through dialogues, it is crucial for systems to ask questions that do not diminish the user’s willingness to talk, i.e., that do not degrade the user’s impression. This paper reports the results of our analysis on how user impression changes depending on the types of questions to acquire lexical knowledge, that is, explicit and implicit questions, and the correctness of the content of the questions. We also analyzed how sequences of the same type of questions affect user impression. User impression scores were collected from 104 participants recruited via crowdsourcing and then regression analysis was conducted. The results demonstrate that implicit questions give a good impression when their content is correct, but a bad impression otherwise. We also found that consecutive explicit questions are more annoying than implicit ones when the content of the questions is correct. Our findings reveal helpful insights for creating a strategy to avoid user impression deterioration during knowledge acquisition.
Simulating Turn-Taking in Conversations with Delayed Transmission
Authors: Thilo Michael, Sebastian Möller
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7

Conversations over the telephone require timely turn-taking cues that signal the participants when to speak and when to listen. When a two-way transmission delay is introduced into such conversations, the immediate feedback is delayed, and the interactivity of the conversation is impaired. With delayed speech on each side of the transmission, different conversation realities emerge on both ends, which alters the way the participants interact with each other. Simulating conversations can give insights on turn-taking and spoken interactions between humans but can also used for analyzing and even predicting human behavior in conversations. In this paper, we simulate two types of conversations with distinct levels of interactivity. We then introduce three levels of two-way transmission delay between the agents and compare the resulting interaction-patterns with human-to-human dialog from an empirical study. We show how the turn-taking mechanisms modeled for conversations without delay perform in scenarios with delay and identify to which extend the simulation is able to model the delayed turn-taking observed in human conversation.
Analyzing Speaker Strategy in Referential Communication
Authors: Brian McMahan, Matthew Stone
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7

We analyze a corpus of referential communication through the lens of quantitative models of speaker reasoning. Different models place different emphases on linguistic reasoning and collaborative reasoning. This leads models to make different assessments of the risks and rewards of using specific utterances in specific contexts. By fitting a latent variable model to the corpus, we can exhibit utterances that give systematic evidence of the diverse kinds of reasoning speakers employ, and build integrated models that recognize not only speaker reference but also speaker reasoning.
How Self-Attention Improves Rare Class Performance in a Question-Answering Dialogue Agent
Authors: Adam Stiff, Qi Song, Eric Fosler-Lussier
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7
Tasks: Language Modeling, Language Modelling, Question Answering, Word Embeddings

Contextualized language modeling using deep Transformer networks has been applied to a variety of natural language processing tasks with remarkable success. However, we find that these models are not a panacea for a question-answering dialogue agent corpus task, which has hundreds of classes in a long-tailed frequency distribution, with only thousands of data points. Instead, we find substantial improvements in recall and accuracy on rare classes from a simple one-layer RNN with multi-headed self-attention and static word embeddings as inputs. While much research has used attention weights to illustrate what input is important for a task, the complexities of our dialogue corpus offer a unique opportunity to examine how the model represents what it attends to, and we offer a detailed analysis of how that contributes to improved performance on rare classes. A particularly interesting phenomenon we observe is that the model picks up implicit meanings by splitting different aspects of the semantics of a single word across multiple attention heads.
Filtering conversations through dialogue acts labels for improving corpus-based convergence studies
Authors: Simone Fuscone, Benoit Favre, Laurent Prévot
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7

Cognitive models of conversation and research on user-adaptation in dialogue systems involves a better understanding of speakers convergence in conversation. Convergence effects have been established on controlled data sets, for various acoustic and linguistic variables. Tracking interpersonal dynamics on generic corpora has provided positive but more contrasted outcomes. We propose here to enrich large conversational corpora with dialogue act (DA) information. We use DA-labels as filters in order to create data sub sets featuring homogeneous conversational activity. Those data sets allow a more precise comparison between speakers’ speech variables. Our experiences consist of comparing convergence on low level variables (Energy, Pitch, Speech Rate) measured on raw data sets, with human and automatically DA-labelled data sets. We found that such filtering does help in observing convergence suggesting that studies on interpersonal dynamics should consider such high level dialogue activity types and their related NLP topics as important ingredients of their toolboxes.
Nontrivial Lexical Convergence in a Geography-Themed Game
Authors: Amanda Bergqvist, Ramesh Manuvinakurike, Deepthi Karkada, Maike Paetzel
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7

The present study aims to examine the prevalent notion that people entrain to the vocabulary of a dialogue system. Although previous research shows that people will replace their choice of words with simple substitutes, studies using more challenging substitutions are sparse. In this paper, we investigate whether people adapt their speech to the vocabulary of a dialogue system when the system’s suggested words are not direct synonyms. 32 participants played a geography-themed game with a remote-controlled agent and were primed by referencing strategies (rather than individual terms) introduced in follow-up questions. Our results suggest that context-appropriate substitutes support convergence and that the convergence has a lasting effect within a dialogue session if the system’s wording is more consistent with the norms of the domain than the original wording of the speaker.
A unifying framework for modeling acoustic/prosodic entrainment: definition and evaluation on two large corpora
Authors: Ramiro H. Gálvez, Lara Gauder, Jordi Luque, Agustín Gravano
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7

Acoustic/prosodic (a/p) entrainment has been associated with multiple positive social aspects of human-human conversations. However, research on its effects is still preliminary, first because how to model it is far from standardized, and second because most of the reported findings rely on small corpora or on corpora collected in experimental setups. The present article has a twofold purpose: 1) it proposes a unifying statistical framework for modeling a/p entrainment, and 2) it tests on two large corpora of spontaneous telephone interactions whether three metrics derived from this framework predict positive social aspects of the conversations. The corpora differ in their spoken language, domain, and positive social outcome attached. To our knowledge, this is the first article studying relations between a/p entrainment and positive social outcomes in such large corpora of spontaneous dialog. Our results suggest that our metrics effectively predict, up to some extent, positive social aspects of conversations, which not only validates the methodology, but also provides further insights into the elusive topic of entrainment in human-human conversation.
Human-Human Health Coaching via Text Messages: Corpus, Annotation, and Analysis
Authors: Itika Gupta, Barbara Di Eugenio, Brian Ziebart, Aiswarya Baiju, Bing Liu, Ben Gerber, Lisa Sharp, Nadia Nabulsi, Mary Smart
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7

Our goal is to develop and deploy a virtual assistant health coach that can help patients set realistic physical activity goals and live a more active lifestyle. Since there is no publicly shared dataset of health coaching dialogues, the first phase of our research focused on data collection. We hired a certified health coach and 28 patients to collect the first round of human-human health coaching interaction which took place via text messages. This resulted in 2853 messages. The data collection phase was followed by conversation analysis to gain insight into the way information exchange takes place between a health coach and a patient. This was formalized using two annotation schemas: one that focuses on the goals the patient is setting and another that models the higher-level structure of the interactions. In this paper, we discuss these schemas and briefly talk about their application for automatically extracting activity goals and annotating the second round of data, collected with different health coaches and patients. Given the resource-intensive nature of data annotation, successfully annotating a new dataset automatically is key to answer the need for high quality, large datasets.