Research on speech understanding and related areas at SRI

Research capabilities on speech understanding, speech recognition, and voice control are described. Research activities and the activities which involve text input rather than speech are discussed

Evaluating Conversational Recommender Systems via User Simulation

Conversational information access is an emerging research area. Currently, human evaluation is used for end-to-end system evaluation, which is both very time and resource intensive at scale, and thus becomes a bottleneck of progress. As an alternative, we propose automated evaluation by means of simulating users. Our user simulator aims to generate responses that a real human would give by considering both individual preferences and the general flow of interaction with the system. We evaluate our simulation approach on an item recommendation task by comparing three existing conversational recommender systems. We show that preference modeling and task-specific interaction models both contribute to more realistic simulations, and can help achieve high correlation between automatic evaluation measures and manual human assessments.Comment: Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '20), 202

Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds

We propose a computational model of situated language comprehension based on the Indexical Hypothesis that generates meaning representations by translating amodal linguistic symbols to modal representations of beliefs, knowledge, and experience external to the linguistic system. This Indexical Model incorporates multiple information sources, including perceptions, domain knowledge, and short-term and long-term experiences during comprehension. We show that exploiting diverse information sources can alleviate ambiguities that arise from contextual use of underspecific referring expressions and unexpressed argument alternations of verbs. The model is being used to support linguistic interactions in Rosie, an agent implemented in Soar that learns from instruction.Comment: Advances in Cognitive Systems 3 (2014

カイワ ダイアログ アンショウ ニ ジュウジ サセル ガイコクゴ シドウホウ ガ スピｰキングジ ノ テイケイ ヒョウゲン ノ シヨウ ト アンキ ガクシュウ ニ オヨボス エイキョウ ニ カンスル キソ ケンキュウ

PDF/A formatsAccess: via World Wide Web東京外国語大学大学院総合国際学研究科博士 (学術) 論文 (2016年4月)Author's thesis (Ph.D)--Tokyo University of Foreign Studies, 2016博甲第214号Bibliography: p. 183-195Summary in English and Japanese東京外国語大学 (Tokyo University of Foreign Studies)博士 (学術

Dialog State Tracking: A Neural Reading Comprehension Approach

Dialog state tracking is used to estimate the current belief state of a dialog given all the preceding conversation. Machine reading comprehension, on the other hand, focuses on building systems that read passages of text and answer questions that require some understanding of passages. We formulate dialog state tracking as a reading comprehension task to answer the question $what\ is\ the\ state\ of\ the\ current\ dialog?$ after reading conversational context. In contrast to traditional state tracking methods where the dialog state is often predicted as a distribution over a closed set of all the possible slot values within an ontology, our method uses a simple attention-based neural network to point to the slot values within the conversation. Experiments on MultiWOZ-2.0 cross-domain dialog dataset show that our simple system can obtain similar accuracies compared to the previous more complex methods. By exploiting recent advances in contextual word embeddings, adding a model that explicitly tracks whether a slot value should be carried over to the next turn, and combining our method with a traditional joint state tracking method that relies on closed set vocabulary, we can obtain a joint-goal accuracy of $47.33\%$ on the standard test split, exceeding current state-of-the-art by $11.75\%$**.Comment: 10 pages, to appear in Special Interest Group on Discourse and Dialogue (SIGDIAL) 2019 (ORAL