Developing conversational systems that can converse in many languages is an interesting challenge for natural language processing. In this paper, we introduce multilingual addressee and response selection. In this task, a conversational system predicts an appropriate addressee and response for an input message in multiple languages. A key to developing such multilingual responding systems is how to utilize high-resource language data to compensate for low-resource language data. We present several knowledge transfer methods for conversational systems. To evaluate our methods, we create a new multilingual conversation dataset. Experiments on the dataset demonstrate the effectiveness of our methods.
* Addressee and Response Selection NUC focuses on two-party, multi-turn conversation . As an extension of it, proposed addressee and response selection (ARS) for multi-party conversation . ARS integrates the addressee detection problem, which has been regarded as a problematic issue in multi-party conversation @cite_21 @cite_0 @cite_8 @cite_6 . Mainly, this problem has been tackled in spoken multimodal dialog systems @cite_25 @cite_3 @cite_5 @cite_4 . While these systems largely rely on acoustic signal or gaze information, ARS focuses on text-based conversations. Extending these studies, we tackle multilingual, multi-turn, and multi-party text conversation settings.
@cite_4 @cite_8 @cite_21 @cite_6 @cite_3 @cite_0 @cite_5 @cite_25
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Addressee detection for dialog systems aims to detect which utterances are directed at the system, as opposed to someone else. An important means for classification is the lexical content of the utterance, and N-gram models have been shown to be effective for this task. In this paper we investigate whether neural networks can enhance lexical addressee detection, using data from a human-human-computer dialog system. Even though we find no improvement from simply replacing the standard Ngram LM with a neural-network LM as class likelihood estimators, improved classification accuracy can be obtained from a modified neural net model that learns distributed word representations in a first training phase, and is trained on the utterance classification task in a second phase. We obtain additional gains by combining the class likelihood estimation and classification training criteria in the second phase, and by combining multiple model architectures at the score level. Overall, we achieve over 2 absolute reduction in equal error rate over the N-gram model baseline of 27 . We report on an empirical study of a multiparty turn-taking model for physically situated spoken dialog systems. We present subjective and objective performance measures that show how the model, supported with a basic set of sensory competencies and turn-taking policies, can enable interactions with multiple participants in a collaborative task setting. The analysis brings to the fore several phenomena and frames challenges for managing multiparty turn taking in physically situated interaction. This article examines some of the issues in representation of, processing, and automated agent participation in natural language dialogue, considering expansion from two-party dialogue to multi-party dialogue. These issues include Some regarding the roles agents play in dialogue, interactive factors, and content management factors. We survey research on the analysis of multiparticipant chat. Multiple research and applied communities (e.g., AI, educational, law enforcement, military) have interest in this topic. After introducing some context, we describe relevant problems and how these have been addressed using AI techniques. We also identify recent research trends and unresolved issues that could benefit from more attention. Several algorithms have recently been proposed for recognizing addressees in a group conversational setting. These algorithms can rely on a variety of factors including previous conversational roles, gaze and type of dialogue act. Both statistical supervised machine learning algorithms as well as rule based methods have been developed. In this paper, we compare several algorithms developed for several different genres of muliparty dialogue, and propose a new synthesis algorithm that matches the performance of machine learning algorithms while maintaning the transparancy of semantically meaningfull rule-based algorithms. In conversational agents with multiparty communication functionality, a system needs to be able to identify the addressee for the current floor and respond to the user when the utterance is addressed to the agent. This study proposes some addressee identification models based on speech and gaze information, and tests whether the models can be applied to different proxemics. We build an addressee identification mechanism by implementing the models and incorporate it into a fully autonomous multiparty conversational agent. The system identifies the addressee from online multimodal data and uses this information in language understanding and dialogue management. Finally, an evaluation experiment shows that the proposed addressee identification mechanism works well in a real-time system, with an F-measure for addressee estimation of 0.8 for agent-addressed utterances. We also found that our system more successfully avoided disturbing the conversation by mistakenly taking a turn when the agent is not addressed.