pwc-archive/papers-with-abstracts huggingface dataset:
Language-Guided Adaptive Perception for Efficient Grounded Communication with Robotic Manipulators in Cluttered Environments
Authors: Siddharth Patki, Thomas Howard
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Robot Manipulation

The utility of collaborative manipulators for shared tasks is highly dependent on the speed and accuracy of communication between the human and the robot. The run-time of recently developed probabilistic inference models for situated symbol grounding of natural language instructions depends on the complexity of the representation of the environment in which they reason. As we move towards more complex bi-directional interactions, tasks, and environments, we need intelligent perception models that can selectively infer precise pose, semantics, and affordances of the objects when inferring exhaustively detailed world models is inefficient and prohibits real-time interaction with these robots. In this paper we propose a model of language and perception for the problem of adapting the configuration of the robot perception pipeline for tasks where constructing exhaustively detailed models of the environment is inefficient and inconsequential for symbol grounding. We present experimental results from a synthetic corpus of natural language instructions for robot manipulation in example environments. The results demonstrate that by adapting perception we get significant gains in terms of run-time for perception and situated symbol grounding of the language instructions without a loss in the accuracy of the latter.
Unsupervised Counselor Dialogue Clustering for Positive Emotion Elicitation in Neural Dialogue System
Authors: Nurul Lubis, Sakriani Sakti, Koichiro Yoshino, Satoshi Nakamura
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Clustering, Emotion Recognition, Goal-Oriented Dialogue Systems, Response Generation

Positive emotion elicitation seeks to improve user{'}s emotional state through dialogue system interaction, where a chat-based scenario is layered with an implicit goal to address user{'}s emotional needs. Standard neural dialogue system approaches still fall short in this situation as they tend to generate only short, generic responses. Learning from expert actions is critical, as these potentially differ from standard dialogue acts. In this paper, we propose using a hierarchical neural network for response generation that is conditioned on 1) expert{'}s action, 2) dialogue context, and 3) user emotion, encoded from user input. We construct a corpus of interactions between a counselor and 30 participants following a negative emotional exposure to learn expert actions and responses in a positive emotion elicitation scenario. Instead of relying on the expensive, labor intensive, and often ambiguous human annotations, we unsupervisedly cluster the expert{'}s responses and use the resulting labels to train the network. Our experiments and evaluation show that the proposed approach yields lower perplexity and generates a larger variety of responses.
A Context-aware Convolutional Natural Language Generation model for Dialogue Systems
Authors: Sourab Mangrulkar, Suhani Shrivastava, Veena Thenkanidiyoor, Dileep Aroor Dinesh
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Machine Translation, Semantic correspondence, Spoken Dialogue Systems, Text Generation, Translation

Natural language generation (NLG) is an important component in spoken dialog systems (SDSs). A model for NLG involves sequence to sequence learning. State-of-the-art NLG models are built using recurrent neural network (RNN) based sequence to sequence models (Ond{\v{r}}ej Du{\v{s}}ek and Filip Jur{\v{c}}{\'\i}{\v{c}}ek, 2016a). Convolutional sequence to sequence based models have been used in the domain of machine translation but their application as Natural Language Generators in dialogue systems is still unexplored. In this work, we propose a novel approach to NLG using convolutional neural network (CNN) based sequence to sequence learning. CNN-based approach allows to build a hierarchical model which encapsulates dependencies between words via shorter path unlike RNNs. In contrast to recurrent models, convolutional approach allows for efficient utilization of computational resources by parallelizing computations over all elements, and eases the learning process by applying constant number of nonlinearities. We also propose to use CNN-based reranker for obtaining responses having semantic correspondence with input dialogue acts. The proposed model is capable of entrainment. Studies using a standard dataset shows the effectiveness of the proposed CNN-based approach to NLG.
A Unified Neural Architecture for Joint Dialog Act Segmentation and Recognition in Spoken Dialog System
Authors: Tianyu Zhao, Tatsuya Kawahara
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, Response Generation, Segmentation, speech-recognition, Speech Recognition, Spoken Language Understanding

In spoken dialog systems (SDSs), dialog act (DA) segmentation and recognition provide essential information for response generation. A majority of previous works assumed ground-truth segmentation of DA units, which is not available from automatic speech recognition (ASR) in SDS. We propose a unified architecture based on neural networks, which consists of a sequence tagger for segmentation and a classifier for recognition. The DA recognition model is based on hierarchical neural networks to incorporate the context of preceding sentences. We investigate sharing some layers of the two components so that they can be trained jointly and learn generalized features from both tasks. An evaluation on the Switchboard Dialog Act (SwDA) corpus shows that the jointly-trained models outperform independently-trained models, single-step models, and other reported results in DA segmentation, recognition, and joint tasks.
Cost-Sensitive Active Learning for Dialogue State Tracking
Authors: Kaige Xie, Cheng Chang, Liliang Ren, Lu Chen, Kai Yu
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Active Learning, Dialogue State Tracking

Dialogue state tracking (DST), when formulated as a supervised learning problem, relies on labelled data. Since dialogue state annotation usually requires labelling all turns of a single dialogue and utilizing context information, it is very expensive to annotate all available unlabelled data. In this paper, a novel cost-sensitive active learning framework is proposed based on a set of new dialogue-level query strategies. This is the first attempt to apply active learning for dialogue state tracking. Experiments on DSTC2 show that active learning with mixed data query strategies can effectively achieve the same DST performance with significantly less data annotation compared to traditional training approaches.
Neural Dialogue Context Online End-of-Turn Detection
Authors: Ryo Masumura, Tomohiro Tanaka, Atsushi Ando, Ryo Ishii, Ryuichiro Higashinaka, Yushi Aono
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Action Detection, Spoken Dialogue Systems

This paper proposes a fully neural network based dialogue-context online end-of-turn detection method that can utilize long-range interactive information extracted from both speaker{'}s utterances and collocutor{'}s utterances. The proposed method combines multiple time-asynchronous long short-term memory recurrent neural networks, which can capture speaker{'}s and collocutor{'}s multiple sequential features, and their interactions. On the assumption of applying the proposed method to spoken dialogue systems, we introduce speaker{'}s acoustic sequential features and collocutor{'}s linguistic sequential features, each of which can be extracted in an online manner. Our evaluation confirms the effectiveness of taking dialogue context formed by the speaker{'}s utterances and collocutor{'}s utterances into consideration.
Spoken Dialogue for Information Navigation
Authors: Alex Papangelis, ros, Panagiotis Papadakos, Yannis Stylianou, Yannis Tzitzikas
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Information Retrieval, slot-filling, Slot Filling, Spoken Dialogue Systems

Aiming to expand the current research paradigm for training conversational AI agents that can address real-world challenges, we take a step away from traditional slot-filling goal-oriented spoken dialogue systems (SDS) and model the dialogue in a way that allows users to be more expressive in describing their needs. The goal is to help users make informed decisions rather than being fed matching items. To this end, we describe the Linked-Data SDS (LD-SDS), a system that exploits semantic knowledge bases that connect to linked data, and supports complex constraints and preferences. We describe the required changes in language understanding and state tracking, and the need for mined features, and we report the promising results (in terms of semantic errors, effort, etc) of a preliminary evaluation after training two statistical dialogue managers in various conditions.
Improving User Impression in Spoken Dialog System with Gradual Speech Form Control
Authors: Yukiko Kageyama, Yuya Chiba, Takashi Nose, Akinori Ito
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Form

This paper examines a method to improve the user impression of a spoken dialog system by introducing a mechanism that gradually changes form of utterances every time the user uses the system. In some languages, including Japanese, the form of utterances changes corresponding to social relationship between the talker and the listener. Thus, this mechanism can be effective to express the system{'}s intention to make social distance to the user closer; however, an actual effect of this method is not investigated enough when introduced to the dialog system. In this paper, we conduct dialog experiments and show that controlling the form of system utterances can improve the users{'} impression.
A Bilingual Interactive Human Avatar Dialogue System
Authors: Dana Abu Ali, Muaz Ahmad, Hayat Al Hassan, Paula Dozsa, Ming Hu, Jose Varias, Nizar Habash
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Answer Selection, Automatic Speech Recognition (ASR), Speech Recognition

This demonstration paper presents a bilingual (Arabic-English) interactive human avatar dialogue system. The system is named TOIA (time-offset interaction application), as it simulates face-to-face conversations between humans using digital human avatars recorded in the past. TOIA is a conversational agent, similar to a chat bot, except that it is based on an actual human being and can be used to preserve and tell stories. The system is designed to allow anybody, simply using a laptop, to create an avatar of themselves, thus facilitating cross-cultural and cross-generational sharing of narratives to wider audiences. The system currently supports monolingual and cross-lingual dialogues in Arabic and English, but can be extended to other languages.
DialCrowd: A toolkit for easy dialog system assessment
Authors: Kyusong Lee, Tiancheng Zhao, Alan W. black, Maxine Eskenazi
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Chatbot

When creating a dialog system, developers need to test each version to ensure that it is performing correctly. Recently the trend has been to test on large datasets or to ask many users to try out a system. Crowdsourcing has solved the issue of finding users, but it presents new challenges such as how to use a crowdsourcing platform and what type of test is appropriate. DialCrowd has been designed to make system assessment easier and to ensure the quality of the result. This paper describes DialCrowd, what specific needs it fulfills and how it works. It then relates a test of DialCrowd by a group of dialog system developer.
Leveraging Multimodal Dialog Technology for the Design of Automated and Interactive Student Agents for Teacher Training
Authors: David Pautler, Vikram Ramanarayanan, Kirby Cofino, Patrick Lange, David Suendermann-Oeft
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Math

We present a paradigm for interactive teacher training that leverages multimodal dialog technology to puppeteer custom-designed embodied conversational agents (ECAs) in student roles. We used the open-source multimodal dialog system HALEF to implement a small-group classroom math discussion involving Venn diagrams where a human teacher candidate has to interact with two student ECAs whose actions are controlled by the dialog system. Such an automated paradigm has the potential to be extended and scaled to a wide range of interactive simulation scenarios in education, medicine, and business where group interaction training is essential.
An Empirical Study of Self-Disclosure in Spoken Dialogue Systems
Authors: Ravich, Abhilasha er, Alan W. Black
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Spoken Dialogue Systems

Self-disclosure is a key social strategy employed in conversation to build relations and increase conversational depth. It has been heavily studied in psychology and linguistic literature, particularly for its ability to induce self-disclosure from the recipient, a phenomena known as reciprocity. However, we know little about how self-disclosure manifests in conversation with automated dialog systems, especially as any self-disclosure on the part of a dialog system is patently disingenuous. In this work, we run a large-scale quantitative analysis on the effect of self-disclosure by analyzing interactions between real-world users and a spoken dialog system in the context of social conversation. We find that indicators of reciprocity occur even in human-machine dialog, with far-reaching implications for chatbots in a variety of domains including education, negotiation and social dialog.
Role play-based question-answering by real users for building chatbots with consistent personalities
Authors: Ryuichiro Higashinaka, Masahiro Mizukami, Hidetoshi Kawabata, Emi Yamaguchi, Noritake Adachi, Junji Tomita
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Chatbot, Question Answering

Having consistent personalities is important for chatbots if we want them to be believable. Typically, many question-answer pairs are prepared by hand for achieving consistent responses; however, the creation of such pairs is costly. In this study, our goal is to collect a large number of question-answer pairs for a particular character by using role play-based question-answering in which multiple users play the roles of certain characters and respond to questions by online users. Focusing on two famous characters, we conducted a large-scale experiment to collect question-answer pairs by using real users. We evaluated the effectiveness of role play-based question-answering and found that, by using our proposed method, the collected pairs lead to good-quality chatbots that exhibit consistent personalities.
Conversational Image Editing: Incremental Intent Identification in a New Dialogue Task
Authors: Ramesh Manuvinakurike, Trung Bui, Walter Chang, Kallirroi Georgila
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: General Classification

We present {``}conversational image editing{''}, a novel real-world application domain combining dialogue, visual information, and the use of computer vision. We discuss the importance of dialogue incrementality in this task, and build various models for incremental intent identification based on deep learning and traditional classification algorithms. We show how our model based on convolutional neural networks outperforms models based on random forests, long short term memory networks, and conditional random fields. By training embeddings based on image-related dialogue corpora, we outperform pre-trained out-of-the-box embeddings, for intention identification tasks. Our experiments also provide evidence that incremental intent processing may be more efficient for the user and could save time in accomplishing tasks.
Fine-Grained Discourse Structures in Continuation Semantics
Authors: Timoth{\'e}e Bernard
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Machine Translation, Question Answering

In this work, we are interested in the computation of logical representations of discourse. We argue that all discourse connectives are anaphors obeying different sets of constraints and show how this view allows one to account for the semantically parenthetical use of attitude verbs and verbs of report (e.g., think, say) and for sequences of conjunctions (A CONJ{\_}1 B CONJ{\_}2 C). We implement this proposal in event semantics using de Groote (2006){'}s dynamic framework.
Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks
Authors: Tirthankar Dasgupta, Rupsa Saha, Lipika Dey, Abir Naskar
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Clustering, Feature Engineering, Sentence

In this paper we have proposed a linguistically informed recursive neural network architecture for automatic extraction of cause-effect relations from text. These relations can be expressed in arbitrarily complex ways. The architecture uses word level embeddings and other linguistic features to detect causal events and their effects mentioned within a sentence. The extracted events and their relations are used to build a causal-graph after clustering and appropriate generalization, which is then used for predictive purposes. We have evaluated the performance of the proposed extraction model with respect to two baseline systems,one a rule-based classifier, and the other a conditional random field (CRF) based supervised model. We have also compared our results with related work reported in the past by other authors on SEMEVAL data set, and found that the proposed bi-directional LSTM model enhanced with an additional linguistic layer performs better. We have also worked extensively on creating new annotated datasets from publicly available data, which we are willing to share with the community.
Toward zero-shot Entity Recognition in Task-oriented Conversational Agents
Authors: Marco Guerini, Simone Magnolini, Vevake Balaraman, Bernardo Magnini
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Zero-Shot Learning

We present a domain portable zero-shot learning approach for entity recognition in task-oriented conversational agents, which does not assume any annotated sentences at training time. Rather, we derive a neural model of the entity names based only on available gazetteers, and then apply the model to recognize new entities in the context of user utterances. In order to evaluate our working hypothesis we focus on nominal entities that are largely used in e-commerce to name products. Through a set of experiments in two languages (English and Italian) and three different domains (furniture, food, clothing), we show that the neural gazetteer-based approach outperforms several competitive baselines, with minimal requirements of linguistic features.
Identifying Explicit Discourse Connectives in German
Authors: Peter Bourgonje, Manfred Stede
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Discourse Parsing

We are working on an end-to-end Shallow Discourse Parsing system for German and in this paper focus on the first subtask: the identification of explicit connectives. Starting with the feature set from an English system and a Random Forest classifier, we evaluate our approach on a (relatively small) German annotated corpus, the Potsdam Commentary Corpus. We introduce new features and experiment with including additional training data obtained through annotation projection and achieve an f-score of 83.89.
Feudal Dialogue Management with Jointly Learned Feature Extractors
Authors: I{\~n}igo Casanueva, Pawe{\l} Budzianowski, Stefan Ultes, Florian Kreyssig, Bo-Hsiang Tseng, Yen-chen Wu, Milica Ga{\v{s}}i{\'c}
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Dialogue Management, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Spoken Dialogue Systems

Reinforcement learning (RL) is a promising dialogue policy optimisation approach, but traditional RL algorithms fail to scale to large domains. Recently, Feudal Dialogue Management (FDM), has shown to increase the scalability to large domains by decomposing the dialogue management decision into two steps, making use of the domain ontology to abstract the dialogue state in each step. In order to abstract the state space, however, previous work on FDM relies on handcrafted feature functions. In this work, we show that these feature functions can be learned jointly with the policy model while obtaining similar performance, even outperforming the handcrafted features in several environments and domains.
Coherence Modeling Improves Implicit Discourse Relation Recognition
Authors: Noriki Nishida, Hideki Nakayama
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Implicit Discourse Relation Classification, Relation, Transfer Learning

The research described in this paper examines how to learn linguistic knowledge associated with discourse relations from unlabeled corpora. We introduce an unsupervised learning method on text coherence that could produce numerical representations that improve implicit discourse relation recognition in a semi-supervised manner. We also empirically examine two variants of coherence modeling: order-oriented and topic-oriented negative sampling, showing that, of the two, topic-oriented negative sampling tends to be more effective.
Constructing a Lexicon of English Discourse Connectives
Authors: Debopam Das, Tatjana Scheffler, Peter Bourgonje, Manfred Stede
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Machine Translation, Text Summarization

We present a new lexicon of English discourse connectives called DiMLex-Eng, built by merging information from two annotated corpora and an additional list of relation signals from the literature. The format follows the German connective lexicon DiMLex, which provides a cross-linguistically applicable XML schema. DiMLex-Eng contains 149 English connectives, and gives information on syntactic categories, discourse semantics and non-connective uses (if any). We report on the development steps and discuss design decisions encountered in the lexicon expansion phase. The resource is freely available for use in studies of discourse structure and computational applications.
Maximizing SLU Performance with Minimal Training Data Using Hybrid RNN Plus Rule-based Approach
Authors: Takeshi Homma, Adriano S. Arantes, Maria Teresa Gonzalez Diaz, Masahito Togami
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Spoken Language Understanding

Spoken language understanding (SLU) by using recurrent neural networks (RNN) achieves good performances for large training data sets, but collecting large training datasets is a challenge, especially for new voice applications. Therefore, the purpose of this study is to maximize SLU performances, especially for small training data sets. To this aim, we propose a novel CRF-based dialog act selector which chooses suitable dialog acts from outputs of RNN SLU and rule-based SLU. We evaluate the selector by using DSTC2 corpus when RNN SLU is trained by less than 1,000 training sentences. The evaluation demonstrates the selector achieves Micro F1 better than both RNN and rule-based SLUs. In addition, it shows the selector achieves better Macro F1 than RNN SLU and the same Macro F1 as rule-based SLU. Thus, we confirmed our method offers advantages in SLU performances for small training data sets.