pwc-archive/papers-with-abstracts huggingface dataset:
Cooperating Tools for MWE Lexicon Management and Corpus Annotation
Authors: Yuji Matsumoto, Akihiko Kato, Hiroyuki Shindo, Toshio Morita
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Management, POS

We present tools for lexicon and corpus management that offer cooperating functionality in corpus annotation. The former, named Cradle, stores a set of words and expressions where multi-word expressions are defined with their own part-of-speech information and internal syntactic structures. The latter, named ChaKi, manages text corpora with part-of-speech (POS) and syntactic dependency structure annotations. Those two tools cooperate so that the words and multi-word expressions stored in Cradle are directly referred to by ChaKi in conducting corpus annotation, and the words and expressions annotated in ChaKi can be output as a list of lexical entities that are to be stored in Cradle.
``Fingers in the Nose'': Evaluating Speakers' Identification of Multi-Word Expressions Using a Slightly Gamified Crowdsourcing Platform
Authors: Kar{\"e}n Fort, Bruno Guillaume, Matthieu Constant, Nicolas Lef{\`e}bvre, Yann-Alan Pilatte
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

This article presents the results we obtained in crowdsourcing French speakers{'} intuition concerning multi-work expressions (MWEs). We developed a slightly gamified crowdsourcing platform, part of which is designed to test users{'} ability to identify MWEs with no prior training. The participants perform relatively well at the task, with a recall reaching 65{\%} for MWEs that do not behave as function words.
Improving Domain Independent Question Parsing with Synthetic Treebanks
Authors: Halim-Antoine Boukaram, Nizar Habash, Micheline Ziadee, Majd Sakr
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

Automatic syntactic parsing for question constructions is a challenging task due to the paucity of training examples in most treebanks. The near absence of question constructions is due to the dominance of the news domain in treebanking efforts. In this paper, we compare two synthetic low-cost question treebank creation methods with a conventional manual high-cost annotation method in the context of three domains (news questions, political talk shows, and chatbots) for Modern Standard Arabic, a language with relatively low resources and rich morphology. Our results show that synthetic methods can be effective at significantly reducing parsing errors for a target domain without having to invest large resources on manual annotation; and the combination of manual and synthetic methods is our best domain-independent performer.
Edition 1.1 of the PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions
Authors: Carlos Ramisch, Silvio Ricardo Cordeiro, Agata Savary, Veronika Vincze, Verginica Barbu Mititelu, Archna Bhatia, Maja Buljan, C, Marie ito, Polona Gantar, Voula Giouli, Tunga G{\"u}ng{\"o}r, Abdelati Hawwari, Uxoa I{\~n}urrieta, Jolanta Kovalevskait{\.e}, Simon Krek, Timm Lichte, Chaya Liebeskind, Johanna Monti, Carla Parra Escart{\'\i}n, Behrang Qasemizadeh, Renata Ramisch, Nathan Schneider, Ivelina Stoyanova, Ashwini Vaidya, Abigail Walsh
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

This paper describes the PARSEME Shared Task 1.1 on automatic identification of verbal multiword expressions. We present the annotation methodology, focusing on changes from last year{'}s shared task. Novel aspects include enhanced annotation guidelines, additional annotated data for most languages, corpora for some new languages, and new evaluation settings. Corpora were created for 20 languages, which are also briefly discussed. We report organizational principles behind the shared task and the evaluation metrics employed for ranking. The 17 participating systems, their methods and obtained results are also presented and analysed.
CRF-Seq and CRF-DepTree at PARSEME Shared Task 2018: Detecting Verbal MWEs using Sequential and Dependency-Based Approaches
Authors: Erwan Moreau, Ashjan Alsulaimani, Alfredo Maldonado, Carl Vogel
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

This paper describes two systems for detecting Verbal Multiword Expressions (VMWEs) which both competed in the closed track at the PARSEME VMWE Shared Task 2018. CRF-DepTree-categs implements an approach based on the dependency tree, intended to exploit the syntactic and semantic relations between tokens; CRF-Seq-nocategs implements a robust sequential method which requires only lemmas and morphosyntactic tags. Both systems ranked in the top half of the ranking, the latter ranking second for token-based evaluation. The code for both systems is published under the GNU General Public License version 3.0 and is available at \url{http://github.com/erwanm/adapt-vmwe18}.
Deep-BGT at PARSEME Shared Task 2018: Bidirectional LSTM-CRF Model for Verbal Multiword Expression Identification
Authors: G{\"o}zde Berk, Berna Erden, Tunga G{\"u}ng{\"o}r
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

This paper describes the Deep-BGT system that participated to the PARSEME shared task 2018 on automatic identification of verbal multiword expressions (VMWEs). Our system is language-independent and uses the bidirectional Long Short-Term Memory model with a Conditional Random Field layer on top (bidirectional LSTM-CRF). To the best of our knowledge, this paper is the first one that employs the bidirectional LSTM-CRF model for VMWE identification. Furthermore, the gappy 1-level tagging scheme is used for discontiguity and overlaps. Our system was evaluated on 10 languages in the open track and it was ranked the second in terms of the general ranking metric.
GBD-NER at PARSEME Shared Task 2018: Multi-Word Expression Detection Using Bidirectional Long-Short-Term Memory Networks and Graph-Based Decoding
Authors: Tiberiu Boros, Rux Burtica, ra
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Feature Engineering, NER

This paper addresses the issue of multi-word expression (MWE) detection by employing a new decoding strategy inspired after graph-based parsing. We show that this architecture achieves state-of-the-art results with minimum feature-engineering, just by relying on lexicalized and morphological attributes. We validate our approach in a multilingual setting, using standard MWE corpora supplied in the PARSEME Shared Task.
Mumpitz at PARSEME Shared Task 2018: A Bidirectional LSTM for the Identification of Verbal Multiword Expressions
Authors: Rafael Ehren, Timm Lichte, Younes Samih
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, Sentence, Word Embeddings

In this paper, we describe Mumpitz, the system we submitted to the PARSEME Shared task on automatic identification of verbal multiword expressions (VMWEs). Mumpitz consists of a Bidirectional Recurrent Neural Network (BRNN) with Long Short-Term Memory (LSTM) units and a heuristic that leverages the dependency information provided in the PARSEME corpus data to differentiate VMWEs in a sentence. We submitted results for seven languages in the closed track of the task and for one language in the open track. For the open track we used the same system, but with pretrained instead of randomly initialized word embeddings to improve the system performance.
TRAPACC and TRAPACCS at PARSEME Shared Task 2018: Neural Transition Tagging of Verbal Multiword Expressions
Authors: Regina Stodden, Behrang Qasemizadeh, Laura Kallmeyer
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: ARC, Dimensionality Reduction, Lexical Analysis

We describe the TRAPACC system and its variant TRAPACCS that participated in the closed track of the PARSEME Shared Task 2018 on labeling verbal multiword expressions (VMWEs). TRAPACC is a modified arc-standard transition system based on Constant and Nivre{'}s (2016) model of joint syntactic and lexical analysis in which the oracle is approximated using a classifier. For TRAPACC, the classifier consists of a data-independent dimension reduction and a convolutional neural network (CNN) for learning and labelling transitions. TRAPACCS extends TRAPACC by replacing the softmax layer of the CNN with a support vector machine (SVM). We report the results obtained for 19 languages, for 8 of which our system yields the best results compared to other participating systems in the closed-track of the shared task.
TRAVERSAL at PARSEME Shared Task 2018: Identification of Verbal Multiword Expressions Using a Discriminative Tree-Structured Model
Authors: Jakub Waszczuk
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: regression

This paper describes a system submitted to the closed track of the PARSEME shared task (edition 1.1) on automatic identification of verbal multiword expressions (VMWEs). The system represents VMWE identification as a labeling task where one of two labels (MWE or not-MWE) must be predicted for each node in the dependency tree based on local context, including adjacent nodes and their labels. The system relies on multiclass logistic regression to determine the globally optimal labeling of a tree. The system ranked 1st in the general cross-lingual ranking of the closed track systems, according to both official evaluation measures: MWE-based F1 and token-based F1.
VarIDE at PARSEME Shared Task 2018: Are Variants Really as Alike as Two Peas in a Pod?
Authors: Caroline Pasquer, Carlos Ramisch, Agata Savary, Jean-Yves Antoine
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

We describe the VarIDE system (standing for Variant IDEntification) which participated in the edition 1.1 of the PARSEME shared task on automatic identification of verbal multiword expressions (VMWEs). Our system focuses on the task of VMWE variant identification by using morphosyntactic information in the training data to predict if candidates extracted from the test corpus could be idiomatic, thanks to a naive Bayes classifier. We report results for 19 languages.
Veyn at PARSEME Shared Task 2018: Recurrent Neural Networks for VMWE Identification
Authors: Nicolas Zampieri, Manon Scholivet, Carlos Ramisch, Benoit Favre
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, TAG, Word Embeddings

This paper describes the Veyn system, submitted to the closed track of the PARSEME Shared Task 2018 on automatic identification of verbal multiword expressions (VMWEs). Veyn is based on a sequence tagger using recurrent neural networks. We represent VMWEs using a variant of the begin-inside-outside encoding scheme combined with the VMWE category tag. In addition to the system description, we present development experiments to determine the best tagging scheme. Veyn is freely available, covers 19 languages, and was ranked ninth (MWE-based) and eight (Token-based) among 13 submissions, considering macro-averaged F1 across languages.
Changing the Level of Directness in Dialogue using Dialogue Vector Models and Recurrent Neural Networks
Authors: Louisa Pragst, Stefan Ultes
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7

In cooperative dialogues, identifying the intent of ones conversation partner and acting accordingly is of great importance. While this endeavour is facilitated by phrasing intentions as directly as possible, we can observe in human-human communication that a number of factors such as cultural norms and politeness may result in expressing one{'}s intent indirectly. Therefore, in human-computer communication we have to anticipate the possibility of users being indirect and be prepared to interpret their actual meaning. Furthermore, a dialogue system should be able to conform to human expectations by adjusting the degree of directness it uses to improve the user experience. To reach those goals, we propose an approach to differentiate between direct and indirect utterances and find utterances of the opposite characteristic that express the same intent. In this endeavour, we employ dialogue vector models and recurrent neural networks.
Modeling Linguistic and Personality Adaptation for Natural Language Generation
Authors: Zhichao Hu, Jean Fox Tree, Marilyn Walker
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Text Generation

Previous work has shown that conversants adapt to many aspects of their partners{'} language. Other work has shown that while every person is unique, they often share general patterns of behavior. Theories of personality aim to explain these shared patterns, and studies have shown that many linguistic cues are correlated with personality traits. We propose an adaptation measure for adaptive natural language generation for dialogs that integrates the predictions of both personality theories and adaptation theories, that can be applied as a dialog unfolds, on a turn by turn basis. We show that our measure meets criteria for validity, and that adaptation varies according to corpora and task, speaker, and the set of features used to model it. We also produce fine-grained models according to the dialog segmentation or the speaker, and demonstrate the decaying trend of adaptation.
Estimating User Interest from Open-Domain Dialogue
Authors: Michimasa Inaba, Kenichi Takahashi
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Speech Recognition

Dialogue personalization is an important issue in the field of open-domain chat-oriented dialogue systems. If these systems could consider their users{'} interests, user engagement and satisfaction would be greatly improved. This paper proposes a neural network-based method for estimating users{'} interests from their utterances in chat dialogues to personalize dialogue systems{'} responses. We introduce a method for effectively extracting topics and user interests from utterances and also propose a pre-training approach that increases learning efficiency. Our experimental results indicate that the proposed model can estimate user{'}s interest more accurately than baseline approaches.
Does Ability Affect Alignment in Second Language Tutorial Dialogue?
Authors: Arabella Sinclair, Adam Lopez, C. G. Lucas, Dragan Gasevic
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7

The role of alignment between interlocutors in second language learning is different to that in fluent conversational dialogue. Learners gain linguistic skill through increased alignment, yet the extent to which they can align will be constrained by their ability. Tutors may use alignment to teach and encourage the student, yet still must push the student and correct their errors, decreasing alignment. To understand how learner ability interacts with alignment, we measure the influence of ability on lexical priming, an indicator of alignment. We find that lexical priming in learner-tutor dialogues differs from that in conversational and task-based dialogues, and we find evidence that alignment increases with ability and with word complexity.
Just Talking - Modelling Casual Conversation
Authors: Emer Gilmartin, Christian Saam, Carl Vogel, Nick Campbell, Vincent Wade
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7

Casual conversation has become a focus for artificial dialogue applications. Such talk is ubiquitous and its structure differs from that found in the task-based interactions which have been the focus of dialogue system design for many years. It is unlikely that such conversations can be modelled as an extension of task-based talk. We review theories of casual conversation, report on our studies of the structure of casual dialogue, and outline challenges we see for the development of spoken dialog systems capable of carrying on casual friendly conversation in addition to performing well-defined tasks.
Neural User Simulation for Corpus-based Policy Optimisation of Spoken Dialogue Systems
Authors: Florian Kreyssig, I{\~n}igo Casanueva, Pawe{\l} Budzianowski, Milica Ga{\v{s}}i{\'c}
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Dialogue Management, Diversity, Reinforcement Learning, Spoken Dialogue Systems, Task-Oriented Dialogue Systems, User Simulation

User Simulators are one of the major tools that enable offline training of task-oriented dialogue systems. For this task the Agenda-Based User Simulator (ABUS) is often used. The ABUS is based on hand-crafted rules and its output is in semantic form. Issues arise from both properties such as limited diversity and the inability to interface a text-level belief tracker. This paper introduces the Neural User Simulator (NUS) whose behaviour is learned from a corpus and which generates natural language, hence needing a less labelled dataset than simulators generating a semantic output. In comparison to much of the past work on this topic, which evaluates user simulators on corpus-based metrics, we use the NUS to train the policy of a reinforcement learning based Spoken Dialogue System. The NUS is compared to the ABUS by evaluating the policies that were trained using the simulators. Cross-model evaluation is performed i.e. training on one simulator and testing on the other. Furthermore, the trained policies are tested on real users. In both evaluation tasks the NUS outperformed the ABUS.
Introduction method for argumentative dialogue using paired question-answering interchange about personality
Authors: Kazuki Sakai, Ryuichiro Higashinaka, Yuichiro Yoshikawa, Hiroshi Ishiguro, Junji Tomita
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Decision Making, Question Answering

To provide a better discussion experience in current argumentative dialogue systems, it is necessary for the user to feel motivated to participate, even if the system already responds appropriately. In this paper, we propose a method that can smoothly introduce argumentative dialogue by inserting an initial discourse, consisting of question-answer pairs concerning personality. The system can induce interest of the users prior to agreement or disagreement during the main discourse. By disclosing their interests, the users will feel familiarity and motivation to further engage in the argumentative dialogue and understand the system{'}s intent. To verify the effectiveness of a question-answer dialogue inserted before the argument, a subjective experiment was conducted using a text chat interface. The results suggest that inserting the question-answer dialogue enhances familiarity and naturalness. Notably, the results suggest that women more than men regard the dialogue as more natural and the argument as deepened, following an exchange concerning personality.
Automatic Token and Turn Level Language Identification for Code-Switched Text Dialog: An Analysis Across Language Pairs and Corpora
Authors: Vikram Ramanarayanan, Robert Pugh
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Language Identification, Spoken Language Understanding, Text Generation

We examine the efficacy of various feature{--}learner combinations for language identification in different types of text-based code-switched interactions {--} human-human dialog, human-machine dialog as well as monolog {--} at both the token and turn levels. In order to examine the generalization of such methods across language pairs and datasets, we analyze 10 different datasets of code-switched text. We extract a variety of character- and word-based text features and pass them into multiple learners, including conditional random fields, logistic regressors and recurrent neural networks. We further examine the efficacy of novel character-level embedding and GloVe features in improving performance and observe that our best-performing text system significantly outperforms a majority vote baseline across language pairs and datasets.
A Situated Dialogue System for Learning Structural Concepts in Blocks World
Authors: Ian Perera, James Allen, Choh Man Teng, Lucian Galescu
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Transfer Learning

We present a modular, end-to-end dialogue system for a situated agent to address a multimodal, natural language dialogue task in which the agent learns complex representations of block structure classes through assertions, demonstrations, and questioning. The concept to learn is provided to the user through a set of positive and negative visual examples, from which the user determines the underlying constraints to be provided to the system in natural language. The system in turn asks questions about demonstrated examples and simulates new examples to check its knowledge and verify the user{'}s description is complete. We find that this task is non-trivial for users and generates natural language that is varied yet understood by our deep language understanding architecture.
Pardon the Interruption: Managing Turn-Taking through Overlap Resolution in Embodied Artificial Agents
Authors: Felix Gervits, Matthias Scheutz
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Spoken Dialogue Systems

Speech overlap is a common phenomenon in natural conversation and in task-oriented interactions. As human-robot interaction (HRI) becomes more sophisticated, the need to effectively manage turn-taking and resolve overlap becomes more important. In this paper, we introduce a computational model for speech overlap resolution in embodied artificial agents. The model identifies when overlap has occurred and uses timing information, dialogue history, and the agent{'}s goals to generate context-appropriate behavior. We implement this model in a Nao robot using the DIARC cognitive robotic architecture. The model is evaluated on a corpus of task-oriented human dialogue, and we find that the robot can replicate many of the most common overlap resolution behaviors found in the human data.
Turn-Taking Strategies for Human-Robot Peer-Learning Dialogue
Authors: Ranjini Das, Heather Pon-Barry
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7

In this paper, we apply the contribution model of grounding to a corpus of human-human peer-mentoring dialogues. From this analysis, we propose effective turn-taking strategies for human-robot interaction with a teachable robot. Specifically, we focus on (1) how robots can encourage humans to present and (2) how robots can signal that they are going to begin a new presentation. We evaluate the strategies against a corpus of human-robot dialogues and offer three guidelines for teachable robots to follow to achieve more human-like collaborative dialogue.
Predicting Perceived Age: Both Language Ability and Appearance are Important
Authors: Sarah Plane, Ariel Marvasti, Tyler Egan, Casey Kennington
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Language Acquisition

When interacting with robots in a situated spoken dialogue setting, human dialogue partners tend to assign anthropomorphic and social characteristics to those robots. In this paper, we explore the age and educational level that human dialogue partners assign to three different robotic systems, including an un-embodied spoken dialogue system. We found that how a robot speaks is as important to human perceptions as the way the robot looks. Using the data from our experiment, we derived prosodic, emotional, and linguistic features from the participants to train and evaluate a classifier that predicts perceived intelligence, age, and education level.