Dialogue history integration into end-to-end signal-to-concept spoken language understanding systems

This work investigates the embeddings for representing dialog history in spoken language understanding (SLU) systems. We focus on the scenario when the semantic information is extracted directly from the speech signal by means of a single end-to-end neural network model. We proposed to integrate dialogue history into an end-to-end signal-to-concept SLU system. The dialog history is represented in the form of dialog history embedding vectors (so-called h-vectors) and is provided as an additional information to end-to-end SLU models in order to improve the system performance. Three following types of h-vectors are proposed and experimentally evaluated in this paper: (1) supervised-all embeddings predicting bag-of-concepts expected in the answer of the user from the last dialog system response; (2) supervised-freq embeddings focusing on predicting only a selected set of semantic concept (corresponding to the most frequent errors in our experiments); and (3) unsupervised embeddings. Experiments on the MEDIA corpus for the semantic slot filling task demonstrate that the proposed h-vectors improve the model performance.Comment: Accepted for ICASSP 2020 (Submitted: October 21, 2019

Survey on Evaluation Methods for Dialogue Systems

In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost and time intensive. Thus, much work has been put into finding methods, which allow to reduce the involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented dialogue systems, conversational dialogue systems, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class

Classification-and-Ranking Architecture Based on Intentions for Response Generation Systems

Existing response generation accounts only concern with generation of words into sentences, either by means of grammar or statistical distribution. While the resulting utterance may be inarguably sophisticated, the impact may be not as forceful. We believe that the design for response generation requires more than grammar rules or some statistical distributions, but more intuitive in the sense that the response robustly satisfies the intention of input utterance. At the same time the response must maintain coherence and relevance, regardless of the surface presentation. This means that response generation is constrained by the content of intentions, rather than the lexicons and grammar. Statistical techniques, mainly the over generation-and-ranking architecture works well in written language where sentence is the basic unit. However, in spoken language where utterance is the basic unit, the disadvantage becomes critical as spoken language also render intentions, hence short strings may be of equivalent impact. The bias towards shortstrings during ranking is the very limitation of this approach hence leading to our proposed intention-based classification-and-ranking architecture. In this architecture, response is deliberately chosen from dialogue corpus rather than wholly generated, such that it allows short ungrammatical utterances as long as they satisfy the intended meaning of input utterance. The architecture employs two basic components, which is a Bayesian classifier to classify user utterances into response classes based on their pragmatic interpretations, and an Entropic ranker that scores the candidate response utterances according to the semantic content relevant to the user utterance. The high-level, pragmatic knowledge in user utterances are used as features in Bayesian classification to constrain response utterance according to their contextual contributions, therefore, guiding our Maximum Entropy ranking process to find one single response utterance that is most relevant to the input utterance. The proposed architecture is tested on a mixed-initiative, transaction dialogue corpus of 64 conversations in theater information and reservation system. We measure the output of the intention-based response generation based on coherence of the response against the input utterance in the test set. We also tested the architecture on the second body of corpus in emergency planning to warrant the portability of architecture to cross domains. In the essence, intention-based response generation performs better as compared to surface generation because features used in the architecture extend well into pragmatics, beyond the linguistic forms and semantic interpretations

Database technology and the management of multimedia data in Mirror

Multimedia digital libraries require an open distributed architecture instead of a monolithic database system. In the Mirror project, we use the Monet extensible database kernel to manage different representations of multimedia objects. To maintain independence between content, meta-data, and the creation of meta-data, we allow distribution of data and operations using CORBA. This open architecture introduces new problems for data access. From an end user’s perspective, the problem is how to search the available representations to fulfill an actual information need; the conceptual gap between human perceptual processes and the meta-data is too large. From a system’s perspective, several representations of the data may semantically overlap or be irrelevant. We address these problems with an iterative query process and active user participation through relevance feedback. A retrieval model based on inference networks assists the user with query formulation. The integration of this model into the database design has two advantages. First, the user can query both the logical and the content structure of multimedia objects. Second, the use of different data models in the logical and the physical database design provides data independence and allows algebraic query optimization. We illustrate query processing with a music retrieval application

Computational Approach to Anaphora Resolution in Spanish Dialogues

This paper presents an algorithm for identifying noun-phrase antecedents of pronouns and adjectival anaphors in Spanish dialogues. We believe that anaphora resolution requires numerous sources of information in order to find the correct antecedent of the anaphor. These sources can be of different kinds, e.g., linguistic information, discourse/dialogue structure information, or topic information. For this reason, our algorithm uses various different kinds of information (hybrid information). The algorithm is based on linguistic constraints and preferences and uses an anaphoric accessibility space within which the algorithm finds the noun phrase. We present some experiments related to this algorithm and this space using a corpus of 204 dialogues. The algorithm is implemented in Prolog. According to this study, 95.9% of antecedents were located in the proposed space, a precision of 81.3% was obtained for pronominal anaphora resolution, and 81.5% for adjectival anaphora