SLU for Voice Command in Smart Home: Comparison of Pipeline and EndtoEnd Approaches Spoken Language Understanding (SLU) is typically performed through automatic speech recognition (ASR) and natural language understanding (NLU) in a pipeline. However, errors at the ASR stage have a negative impact on the NLU performance. Hence, there is a rising interest in EndtoEnd (E2E) SLU to jointly perform ASR and NLU. Although E2E models have shown superior performance to modular approaches in many NLP tasks, current SLU E2E models have still not definitely superseded pipeline approaches. In this paper, we present a comparison of the pipeline and E2E approaches for the task of voice command in smart homes. Since there are no large nonEnglish domainspecific data sets available, although needed for an E2E model, we tackle the lack of such data by combining Natural Language Generation (NLG) and texttospeech (TTS) to generate French training data. The trained models were evaluated on voice commands acquired in a real smart home with several speakers. Results show that the E2E approach can reach performances similar to a stateofthe art pipeline SLU despite a higher WER than the pipeline approach. Furthermore, the E2E model can benefit from artificially generated data to exhibit lower Concept Error Rates than the pipeline baseline for slot recognition.

Sequential Neural Networks for Noetic EndtoEnd Response Selection Abstract   The noetic endtoend response selection challenge as one track in the 7th Dialog System Technology Challenges (DSTC7) aims to push the state of the art of utterance classification for real world goaloriented dialog systems, for which participants need to select the correct next utterances from a set of candidates for the multiturn context. This paper presents our systems that are ranked top 1 on both datasets under this challenge, one focused and small (Advising) and the other more diverse and large (Ubuntu). Previous stateoftheart models use hierarchybased (utterancelevel and tokenlevel) neural networks to explicitly model the interactions among different turnsxe2x80x99 utterances for context modeling. In this paper, we investigate a sequential matching model based only on chain sequence for multiturn response selection. Our results demonstrate that the potentials of sequential matching approaches have not yet been fully exploited in the past for multiturn response selection. In addition to ranking top 1 in the challenge, the proposed model outperforms all previous models, including stateoftheart hierarchybased models, on two largescale public multiturn response selection benchmark datasets.