498 research outputs found
A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling
A spoken language understanding (SLU) system includes two main tasks, slot
filling (SF) and intent detection (ID). The joint model for the two tasks is
becoming a tendency in SLU. But the bi-directional interrelated connections
between the intent and slots are not established in the existing joint models.
In this paper, we propose a novel bi-directional interrelated model for joint
intent detection and slot filling. We introduce an SF-ID network to establish
direct connections for the two tasks to help them promote each other mutually.
Besides, we design an entirely new iteration mechanism inside the SF-ID network
to enhance the bi-directional interrelated connections. The experimental
results show that the relative improvement in the sentence-level semantic frame
accuracy of our model is 3.79% and 5.42% on ATIS and Snips datasets,
respectively, compared to the state-of-the-art model.Comment: Accepted paper of ACL 2019 (short paper) with 5 page
Joint Intent Detection and Slot Filling with Wheel-Graph Attention Networks
Intent detection and slot filling are two fundamental tasks for building a
spoken language understanding (SLU) system. Multiple deep learning-based joint
models have demonstrated excellent results on the two tasks. In this paper, we
propose a new joint model with a wheel-graph attention network (Wheel-GAT)
which is able to model interrelated connections directly for intent detection
and slot filling. To construct a graph structure for utterances, we create
intent nodes, slot nodes, and directed edges. Intent nodes can provide
utterance-level semantic information for slot filling, while slot nodes can
also provide local keyword information for intent. Experiments show that our
model outperforms multiple baselines on two public datasets. Besides, we also
demonstrate that using Bidirectional Encoder Representation from Transformer
(BERT) model further boosts the performance in the SLU task
A Survey on Spoken Language Understanding: Recent Advances and New Frontiers
Spoken Language Understanding (SLU) aims to extract the semantics frame of
user queries, which is a core component in a task-oriented dialog system. With
the burst of deep neural networks and the evolution of pre-trained language
models, the research of SLU has obtained significant breakthroughs. However,
there remains a lack of a comprehensive survey summarizing existing approaches
and recent trends, which motivated the work presented in this article. In this
paper, we survey recent advances and new frontiers in SLU. Specifically, we
give a thorough review of this research field, covering different aspects
including (1) new taxonomy: we provide a new perspective for SLU filed,
including single model vs. joint model, implicit joint modeling vs. explicit
joint modeling in joint model, non pre-trained paradigm vs. pre-trained
paradigm;(2) new frontiers: some emerging areas in complex SLU as well as the
corresponding challenges; (3) abundant open-source resources: to help the
community, we have collected, organized the related papers, baseline projects
and leaderboard on a public website where SLU researchers could directly access
to the recent progress. We hope that this survey can shed a light on future
research in SLU field.Comment: Survey for SLU Direction. Resources in
\url{https://github.com/yizhen20133868/Awesome-SLU-Survey
Joint Intent Detection And Slot Filling Based on Continual Learning Model
Slot filling and intent detection have become a significant theme in the
field of natural language understanding. Even though slot filling is
intensively associated with intent detection, the characteristics of the
information required for both tasks are different while most of those
approaches may not fully aware of this problem. In addition, balancing the
accuracy of two tasks effectively is an inevitable problem for the joint
learning model. In this paper, a Continual Learning Interrelated Model (CLIM)
is proposed to consider semantic information with different characteristics and
balance the accuracy between intent detection and slot filling effectively. The
experimental results show that CLIM achieves state-of-the-art performace on
slot filling and intent detection on ATIS and Snips.Comment: Accepted to ICASSP 202
PIN: A Novel Parallel Interactive Network for Spoken Language Understanding
Spoken Language Understanding (SLU) is an essential part of the spoken
dialogue system, which typically consists of intent detection (ID) and slot
filling (SF) tasks. Recently, recurrent neural networks (RNNs) based methods
achieved the state-of-the-art for SLU. It is noted that, in the existing
RNN-based approaches, ID and SF tasks are often jointly modeled to utilize the
correlation information between them. However, we noted that, so far, the
efforts to obtain better performance by supporting bidirectional and explicit
information exchange between ID and SF are not well studied.In addition, few
studies attempt to capture the local context information to enhance the
performance of SF. Motivated by these findings, in this paper, Parallel
Interactive Network (PIN) is proposed to model the mutual guidance between ID
and SF. Specifically, given an utterance, a Gaussian self-attentive encoder is
introduced to generate the context-aware feature embedding of the utterance
which is able to capture local context information. Taking the feature
embedding of the utterance, Slot2Intent module and Intent2Slot module are
developed to capture the bidirectional information flow for ID and SF tasks.
Finally, a cooperation mechanism is constructed to fuse the information
obtained from Slot2Intent and Intent2Slot modules to further reduce the
prediction bias.The experiments on two benchmark datasets, i.e., SNIPS and
ATIS, demonstrate the effectiveness of our approach, which achieves a
competitive result with state-of-the-art models. More encouragingly, by using
the feature embedding of the utterance generated by the pre-trained language
model BERT, our method achieves the state-of-the-art among all comparison
approaches
Multi-Domain Spoken Language Understanding Using Domain- and Task-Aware Parameterization
Spoken language understanding has been addressed as a supervised learning
problem, where a set of training data is available for each domain. However,
annotating data for each domain is both financially costly and non-scalable so
we should fully utilize information across all domains. One existing approach
solves the problem by conducting multi-domain learning, using shared parameters
for joint training across domains. We propose to improve the parameterization
of this method by using domain-specific and task-specific model parameters to
improve knowledge learning and transfer. Experiments on 5 domains show that our
model is more effective for multi-domain SLU and obtain the best results. In
addition, we show its transferability by outperforming the prior best model by
12.4\% when adapting to a new domain with little data.Comment: Accepted by Transactions on Asian and Low-Resource Language
Information Processing (TALLIP
Enriched Pre-trained Transformers for Joint Slot Filling and Intent Detection
Detecting the user's intent and finding the corresponding slots among the
utterance's words are important tasks in natural language understanding. Their
interconnected nature makes their joint modeling a standard part of training
such models. Moreover, data scarceness and specialized vocabularies pose
additional challenges. Recently, the advances in pre-trained language models,
namely contextualized models such as ELMo and BERT have revolutionized the
field by tapping the potential of training very large models with just a few
steps of fine-tuning on a task-specific dataset. Here, we leverage such model,
namely BERT, and we design a novel architecture on top it. Moreover, we propose
an intent pooling attention mechanism, and we reinforce the slot filling task
by fusing intent distributions, word features, and token representations. The
experimental results on standard datasets show that our model outperforms both
the current non-BERT state of the art as well as some stronger BERT-based
baselines
A Result based Portable Framework for Spoken Language Understanding
Spoken language understanding (SLU), which is a core component of the
task-oriented dialogue system, has made substantial progress in the research of
single-turn dialogue. However, the performance in multi-turn dialogue is still
not satisfactory in the sense that the existing multi-turn SLU methods have low
portability and compatibility for other single-turn SLU models. Further,
existing multi-turn SLU methods do not exploit the historical predicted results
when predicting the current utterance, which wastes helpful information. To gap
those shortcomings, in this paper, we propose a novel Result-based Portable
Framework for SLU (RPFSLU). RPFSLU allows most existing single-turn SLU models
to obtain the contextual information from multi-turn dialogues and takes full
advantage of predicted results in the dialogue history during the current
prediction. Experimental results on the public dataset KVRET have shown that
all SLU models in baselines acquire enhancement by RPFSLU on multi-turn SLU
tasks.Comment: ICME202
Injecting Word Information with Multi-Level Word Adapter for Chinese Spoken Language Understanding
In this paper, we improve Chinese spoken language understanding (SLU) by
injecting word information. Previous studies on Chinese SLU do not consider the
word information, failing to detect word boundaries that are beneficial for
intent detection and slot filling. To address this issue, we propose a
multi-level word adapter to inject word information for Chinese SLU, which
consists of (1) sentence-level word adapter, which directly fuses the sentence
representations of the word information and character information to perform
intent detection and (2) character-level word adapter, which is applied at each
character for selectively controlling weights on word information as well as
character information. Experimental results on two Chinese SLU datasets show
that our model can capture useful word information and achieve state-of-the-art
performance.Comment: Accepted at ICASSP 202
A character representation enhanced on-device Intent Classification
Intent classification is an important task in natural language understanding
systems. Existing approaches have achieved perfect scores on the benchmark
datasets. However they are not suitable for deployment on low-resource devices
like mobiles, tablets, etc. due to their massive model size. Therefore, in this
paper, we present a novel light-weight architecture for intent classification
that can run efficiently on a device. We use character features to enrich the
word representation. Our experiments prove that our proposed model outperforms
existing approaches and achieves state-of-the-art results on benchmark
datasets. We also report that our model has tiny memory footprint of ~5 MB and
low inference time of ~2 milliseconds, which proves its efficiency in a
resource-constrained environment.Comment: Accepted for publication in ICON 2020: 17th International Conference
on Natural Language Processin
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