545 research outputs found
Deep Cascade Multi-task Learning for Slot Filling in Online Shopping Assistant
Slot filling is a critical task in natural language understanding (NLU) for
dialog systems. State-of-the-art approaches treat it as a sequence labeling
problem and adopt such models as BiLSTM-CRF. While these models work relatively
well on standard benchmark datasets, they face challenges in the context of
E-commerce where the slot labels are more informative and carry richer
expressions. In this work, inspired by the unique structure of E-commerce
knowledge base, we propose a novel multi-task model with cascade and residual
connections, which jointly learns segment tagging, named entity tagging and
slot filling. Experiments show the effectiveness of the proposed cascade and
residual structures. Our model has a 14.6% advantage in F1 score over the
strong baseline methods on a new Chinese E-commerce shopping assistant dataset,
while achieving competitive accuracies on a standard dataset. Furthermore,
online test deployed on such dominant E-commerce platform shows 130%
improvement on accuracy of understanding user utterances. Our model has already
gone into production in the E-commerce platform.Comment: AAAI 201
Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems
Language understanding is a key component in a spoken dialogue system. In
this paper, we investigate how the language understanding module influences the
dialogue system performance by conducting a series of systematic experiments on
a task-oriented neural dialogue system in a reinforcement learning based
setting. The empirical study shows that among different types of language
understanding errors, slot-level errors can have more impact on the overall
performance of a dialogue system compared to intent-level errors. In addition,
our experiments demonstrate that the reinforcement learning based dialogue
system is able to learn when and what to confirm in order to achieve better
performance and greater robustness.Comment: 5 pages, 5 figure
A Survey on Dialogue Systems: Recent Advances and New Frontiers
Dialogue systems have attracted more and more attention. Recent advances on
dialogue systems are overwhelmingly contributed by deep learning techniques,
which have been employed to enhance a wide range of big data applications such
as computer vision, natural language processing, and recommender systems. For
dialogue systems, deep learning can leverage a massive amount of data to learn
meaningful feature representations and response generation strategies, while
requiring a minimum amount of hand-crafting. In this article, we give an
overview to these recent advances on dialogue systems from various perspectives
and discuss some possible research directions. In particular, we generally
divide existing dialogue systems into task-oriented and non-task-oriented
models, then detail how deep learning techniques help them with representative
algorithms and finally discuss some appealing research directions that can
bring the dialogue system research into a new frontier.Comment: 13 pages. arXiv admin note: text overlap with arXiv:1703.01008 by
other author
End-to-End Slot Alignment and Recognition for Cross-Lingual NLU
Natural language understanding (NLU) in the context of goal-oriented dialog
systems typically includes intent classification and slot labeling tasks.
Existing methods to expand an NLU system to new languages use machine
translation with slot label projection from source to the translated
utterances, and thus are sensitive to projection errors. In this work, we
propose a novel end-to-end model that learns to align and predict target slot
labels jointly for cross-lingual transfer. We introduce MultiATIS++, a new
multilingual NLU corpus that extends the Multilingual ATIS corpus to nine
languages across four language families, and evaluate our method using the
corpus. Results show that our method outperforms a simple label projection
method using fast-align on most languages, and achieves competitive performance
to the more complex, state-of-the-art projection method with only half of the
training time. We release our MultiATIS++ corpus to the community to continue
future research on cross-lingual NLU.Comment: Accepted at EMNLP 202
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
AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
In real-world scenarios, users usually have multiple intents in the same
utterance. Unfortunately, most spoken language understanding (SLU) models
either mainly focused on the single intent scenario, or simply incorporated an
overall intent context vector for all tokens, ignoring the fine-grained
multiple intents information integration for token-level slot prediction. In
this paper, we propose an Adaptive Graph-Interactive Framework (AGIF) for joint
multiple intent detection and slot filling, where we introduce an intent-slot
graph interaction layer to model the strong correlation between the slot and
intents. Such an interaction layer is applied to each token adaptively, which
has the advantage to automatically extract the relevant intents information,
making a fine-grained intent information integration for the token-level slot
prediction. Experimental results on three multi-intent datasets show that our
framework obtains substantial improvement and achieves the state-of-the-art
performance. In addition, our framework achieves new state-of-the-art
performance on two single-intent datasets.Comment: Accepted at Findings of EMNLP 202
Intent Detection with WikiHow
Modern task-oriented dialog systems need to reliably understand users'
intents. Intent detection is most challenging when moving to new domains or new
languages, since there is little annotated data. To address this challenge, we
present a suite of pretrained intent detection models. Our models are able to
predict a broad range of intended goals from many actions because they are
trained on wikiHow, a comprehensive instructional website. Our models achieve
state-of-the-art results on the Snips dataset, the Schema-Guided Dialogue
dataset, and all 3 languages of the Facebook multilingual dialog datasets. Our
models also demonstrate strong zero- and few-shot performance, reaching over
75% accuracy using only 100 training examples in all datasets.Comment: In AACL-IJCNLP 202
Recent Advances and Challenges in Task-oriented Dialog System
Due to the significance and value in human-computer interaction and natural
language processing, task-oriented dialog systems are attracting more and more
attention in both academic and industrial communities. In this paper, we survey
recent advances and challenges in task-oriented dialog systems. We also discuss
three critical topics for task-oriented dialog systems: (1) improving data
efficiency to facilitate dialog modeling in low-resource settings, (2) modeling
multi-turn dynamics for dialog policy learning to achieve better
task-completion performance, and (3) integrating domain ontology knowledge into
the dialog model. Besides, we review the recent progresses in dialog evaluation
and some widely-used corpora. We believe that this survey, though incomplete,
can shed a light on future research in task-oriented dialog systems.Comment: Under review of SCIENCE CHINA Technological Science (SCTS
CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots
Natural Language Understanding (NLU) is a core component of dialog systems.
It typically involves two tasks - intent classification (IC) and slot labeling
(SL), which are then followed by a dialogue management (DM) component. Such NLU
systems cater to utterances in isolation, thus pushing the problem of context
management to DM. However, contextual information is critical to the correct
prediction of intents and slots in a conversation. Prior work on contextual NLU
has been limited in terms of the types of contextual signals used and the
understanding of their impact on the model. In this work, we propose a
context-aware self-attentive NLU (CASA-NLU) model that uses multiple signals,
such as previous intents, slots, dialog acts and utterances over a variable
context window, in addition to the current user utterance. CASA-NLU outperforms
a recurrent contextual NLU baseline on two conversational datasets, yielding a
gain of up to 7% on the IC task for one of the datasets. Moreover, a
non-contextual variant of CASA-NLU achieves state-of-the-art performance for IC
task on standard public datasets - Snips and ATIS.Comment: To appear at EMNLP 201
MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark
Scaling semantic parsing models for task-oriented dialog systems to new
languages is often expensive and time-consuming due to the lack of available
datasets. Available datasets suffer from several shortcomings: a) they contain
few languages b) they contain small amounts of labeled examples per language c)
they are based on the simple intent and slot detection paradigm for
non-compositional queries. In this paper, we present a new multilingual
dataset, called MTOP, comprising of 100k annotated utterances in 6 languages
across 11 domains. We use this dataset and other publicly available datasets to
conduct a comprehensive benchmarking study on using various state-of-the-art
multilingual pre-trained models for task-oriented semantic parsing. We achieve
an average improvement of +6.3 points on Slot F1 for the two existing
multilingual datasets, over best results reported in their experiments.
Furthermore, we demonstrate strong zero-shot performance using pre-trained
models combined with automatic translation and alignment, and a proposed
distant supervision method to reduce the noise in slot label projection.Comment: 13 pages, 2 figures, Accepted at EACL 202
- …