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Language understanding work at Paramax focuses on ap- plying general-purpose language understanding technology to spoken language understanding, text understanding, and document processing, integrating language understanding with speech recognition, knowledge-based information …...

Interpreting fttUy natural speech is an important goal for spoken language understanding systems. However, while corpus studies have shown that about 10% of spontaneous utterances contain self- corrections, or REPAIRS, …...

Interpreting fully natural speech is an important goal for spoken language understanding systems. However, while corpus studies have shown that about 10% of spontaneous utterances contain self-corrections, or RE- PAIRS, …...

In this paper, we present a weakly super- vised learning approach for spoken lan- guage understanding in domain-specific dialogue systems. We model the task of spoken language understanding as a …...

Motivated by psycholinguistic findings, we are currently investigating the role of eye gaze in spoken language understand- ing for multimodal conversational sys- tems. Our assumption is that, during hu- man …...

Spoken Language Understanding aims at mapping a natural language spoken sen- tence into a semantic representation. In the last decade two main approaches have been pursued: native models.generative and discrimi- …...

Robustness and portability of spoken language understanding systems among languages and domains : the PORTMEDIA project The ANR PORTMEDIA projet aimed at complementing the MEDIA corpus so as to foster …...

This paper presents an empirical study on using syntactic and semantic information for Concept Segmentation and Labeling (CSL), a well-known component in spoken language understand- ing. Our approach is based …...

We experiment graph-based Semi- Supervised Learning (SSL) of Conditional Random Fields (CRF) for the application of Spoken Language (SLU) on unaligned data.Understanding The aligned labels for examples are obtained using …...

AbstractThe development of annotated corpora is a critical process in the development of speech applications for multiple target languages. While the technology to develop a monolingual speech application has reached …...

When building spoken dialogue systems for a new domain, a major bottleneck is developing a spoken language understand- ing (SLU) module that handles the new domain’s terminology and semantic con- …...

AbstractWe introduce a dialogue task between a virtual patient and a doctor where the dialogue system, playing the patient part in a simulated consultation, must reconcile a specialized level, to …...

. Recurrent Neural Networks have proved effective on several NLP tasks. Despite such great success, their ability to model sequence labeling is still limited. This lead research toward solutions where …...

AbstractSpoken language understanding (SLU) is an essential component in conversational systems. Most SLU components treats each utterance independently, and then the following components aggregate the multi-turn information in the separate …...

This paper presents an approach to simulate automatic speech recognition (ASR) errors from manual transcriptions and describes how it can be used to improve the performance of spoken language understanding …...

AbstractTo deploy a spoken language understanding (SLU) model to a new language, language transferring is desired to avoid the trouble of acquiring and labeling a new big SLU corpus. An …...

AbstractSpoken Language Understanding (SLU) converts user utterances into structured semantic representations. Data sparsity is one of the main obstacles of SLU due to the high cost of human annotation, especially …...

AbstractThe Air Travel Information Service (ATIS) corpus has been the most common benchmark for evaluating Spoken Language Understanding (SLU) tasks for more than three decades since it was released. Recent …...

AbstractCatastrophic forgetting in neural networks indicates the performance decreasing of deep learning models on previous tasks while learning new tasks. To address this problem, we propose a novel Continual Learning …...

AbstractDespite the promising results of current cross-lingual models for spoken language understanding systems, they still suffer from imperfect cross-lingual representation alignments between the source and target languages, which makes the …...

AbstractAbstractive community detection is an important spoken language understanding task, whose goal is to group utterances in a conversation according to whether they can be jointly summarized by a common …...

AbstractSpoken language understanding (SLU), which converts user requests in natural language to machine-interpretable expressions, is becoming an essential task. The lack of training data is an important problem, especially for …...

AbstractIn this paper, we present a study on a French Spoken Language Understanding (SLU) task: the MEDIA task. Many works and studies have been proposed for many tasks, but most …...

AbstractSlot filling and intent detection are two major tasks for spoken language understanding. In most existing work, these two tasks are built as joint models with multi-task learning with no …...

AbstractThis paper addresses the question as to what degree a BERT-based multilingual Spoken Language Understanding (SLU) model can transfer knowledge across languages. Through experiments we will show that, although it …...

AbstractSpoken language understanding tasks usually rely on pipelines involving complex processing blocks such as voice activity detection, speaker diarization and Automatic speech recognition (ASR). We propose a novel framework for …...

AbstractIntent classification is a major task in spoken language understanding (SLU). Since most models are built with pre-collected in-domain (IND) training utterances, their ability to detect unsupported out-of-domain (OOD) utterances …...

AbstractThe lack of publicly available evaluation data for low-resource languages limits progress in Spoken Language Understanding (SLU). As key tasks like intent classification and slot filling require abundant training data, …...

AbstractLack of training data presents a grand challenge to scaling out spoken language understanding (SLU) to low-resource languages. Although various data augmentation approaches have been proposed to synthesize training data …...

AbstractSpoken language understanding (SLU) requires a model to analyze input acoustic signal to understand its linguistic content and make predictions. To boost the models’ performance, various pre-training methods have been …...

AbstractSpoken language understanding, usually including intent detection and slot filling, is a core component to build a spoken dialog system. Recent research shows promising results by jointly learning of those …...

AbstractIn this study, we investigate robustness against covariate drift in spoken language understanding (SLU). Covariate drift can occur in SLUwhen there is a drift between training and testing regarding what …...

AbstractPretrained models through self-supervised learning have been recently introduced for both acoustic and language modeling. Applied to spoken language understanding tasks, these models have shown their great potential by improving …...

Spoken language understanding (SLU) sys- tems using deep learning techniques require ef- fective intent detection and slot filling models. Previous studies in this field taking advantage of sequence to sequence …...

AbstractDespite the great success of spoken language understanding (SLU) in high-resource languages, it remains challenging in low-resource languages mainly due to the lack of labeled training data. The recent multilingual …...

AbstractSpoken language understanding (SLU) tasks involve mapping from speech signals to semantic labels. Given the complexity of such tasks, good performance is expected to require large labeled datasets, which are …...

AbstractWith the emergence of neural end-to-end approaches for spoken language understanding (SLU), a growing number of studies have been presented during these last three years on this topic. The major …...