SLU FOR VOICE COMMAND IN SMART HOME: COMPARISON OF PIPELINE AND END-TO-END APPROACHES

International audienceSpoken Language Understanding (SLU) is typically performedthrough automatic speech recognition (ASR) andnatural language understanding (NLU) in a pipeline. However,errors at the ASR stage have a negative impact on theNLU performance. Hence, there is a rising interest in End-to-End (E2E) SLU to jointly perform ASR and NLU. AlthoughE2E models have shown superior performance to modularapproaches in many NLP tasks, current SLU E2E modelshave still not definitely superseded pipeline approaches.In this paper, we present a comparison of the pipelineand E2E approaches for the task of voice command in smarthomes. Since there are no large non-English domain-specificdata sets available, although needed for an E2E model, wetackle the lack of such data by combining Natural LanguageGeneration (NLG) and text-to-speech (TTS) to generateFrench training data. The trained models were evaluatedon voice commands acquired in a real smart home with severalspeakers. Results show that the E2E approach can reachperformances similar to a state-of-the art pipeline SLU despitea higher WER than the pipeline approach. Furthermore,the E2E model can benefit from artificially generated data toexhibit lower Concept Error Rates than the pipeline baselinefor slot recognition

Scientific Information Extraction with Semi-supervised Neural Tagging

This paper addresses the problem of extracting keyphrases from scientific articles and categorizing them as corresponding to a task, process, or material. We cast the problem as sequence tagging and introduce semi-supervised methods to a neural tagging model, which builds on recent advances in named entity recognition. Since annotated training data is scarce in this domain, we introduce a graph-based semi-supervised algorithm together with a data selection scheme to leverage unannotated articles. Both inductive and transductive semi-supervised learning strategies outperform state-of-the-art information extraction performance on the 2017 SemEval Task 10 ScienceIE task.Comment: accepted by EMNLP 201

Establishing a New State-of-the-Art for French Named Entity Recognition

The French TreeBank developed at the University Paris 7 is the main source of morphosyntactic and syntactic annotations for French. However, it does not include explicit information related to named entities, which are among the most useful information for several natural language processing tasks and applications. Moreover, no large-scale French corpus with named entity annotations contain referential information, which complement the type and the span of each mention with an indication of the entity it refers to. We have manually annotated the French TreeBank with such information, after an automatic pre-annotation step. We sketch the underlying annotation guidelines and we provide a few figures about the resulting annotations

Towards End-to-End spoken intent recognition in smart home

International audienceVoice based interaction in a smart home has become a feature of many industrial products. These systems react to voice commands, whether it is for answering a question, providing music or turning on the lights. To be efficient, these systems must be able to extract the intent of the user from the voice command. Intent recognition from voice is typically performed through automatic speech recognition (ASR) and intent classification from the transcriptions in a pipeline. However, the errors accumulated at the ASR stage might severely impact the intent classifier. In this paper, we propose an End-to-End (E2E) model to perform intent classification directly from the raw speech input. The E2E approach is thus optimized for this specific task and avoids error propagation. Furthermore, prosodic aspects of the speech signal can be exploited by the E2E model for intent classification (e.g., question vs imperative voice). Experiments on a corpus of voice commands acquired in a real smart home reveal that the state-of-the art pipeline baseline is still superior to the E2E approach. However, using artificial data generation techniques we show that significant improvement to the E2E model can be brought to reach competitive performances. This opens the way to further research on E2E Spoken Language Understanding