312,567 research outputs found
Context-Dependent Semantic Parsing over Temporally Structured Data
We describe a new semantic parsing setting that allows users to query the
system using both natural language questions and actions within a graphical
user interface. Multiple time series belonging to an entity of interest are
stored in a database and the user interacts with the system to obtain a better
understanding of the entity's state and behavior, entailing sequences of
actions and questions whose answers may depend on previous factual or
navigational interactions. We design an LSTM-based encoder-decoder architecture
that models context dependency through copying mechanisms and multiple levels
of attention over inputs and previous outputs. When trained to predict tokens
using supervised learning, the proposed architecture substantially outperforms
standard sequence generation baselines. Training the architecture using policy
gradient leads to further improvements in performance, reaching a
sequence-level accuracy of 88.7% on artificial data and 74.8% on real data.Comment: Accepted by NAACL 2019 (Oral presentation
The Rapidly Changing Landscape of Conversational Agents
Conversational agents have become ubiquitous, ranging from goal-oriented
systems for helping with reservations to chit-chat models found in modern
virtual assistants. In this survey paper, we explore this fascinating field. We
look at some of the pioneering work that defined the field and gradually move
to the current state-of-the-art models. We look at statistical, neural,
generative adversarial network based and reinforcement learning based
approaches and how they evolved. Along the way we discuss various challenges
that the field faces, lack of context in utterances, not having a good
quantitative metric to compare models, lack of trust in agents because they do
not have a consistent persona etc. We structure this paper in a way that
answers these pertinent questions and discusses competing approaches to solve
them.Comment: 14 pages, 7 figures. arXiv admin note: text overlap with
arXiv:1704.07130, arXiv:1507.04808, arXiv:1603.06155, arXiv:1611.06997,
arXiv:1704.08966 by other author
IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles
We present a sequence-to-action parsing approach for the natural language to
SQL task that incrementally fills the slots of a SQL query with feasible
actions from a pre-defined inventory. To account for the fact that typically
there are multiple correct SQL queries with the same or very similar semantics,
we draw inspiration from syntactic parsing techniques and propose to train our
sequence-to-action models with non-deterministic oracles. We evaluate our
models on the WikiSQL dataset and achieve an execution accuracy of 83.7% on the
test set, a 2.1% absolute improvement over the models trained with traditional
static oracles assuming a single correct target SQL query. When further
combined with the execution-guided decoding strategy, our model sets a new
state-of-the-art performance at an execution accuracy of 87.1%
Neural Approaches to Conversational AI
The present paper surveys neural approaches to conversational AI that have
been developed in the last few years. We group conversational systems into
three categories: (1) question answering agents, (2) task-oriented dialogue
agents, and (3) chatbots. For each category, we present a review of
state-of-the-art neural approaches, draw the connection between them and
traditional approaches, and discuss the progress that has been made and
challenges still being faced, using specific systems and models as case
studies.Comment: Foundations and Trends in Information Retrieval (95 pages
Knowledge-Aware Conversational Semantic Parsing Over Web Tables
Conversational semantic parsing over tables requires knowledge acquiring and
reasoning abilities, which have not been well explored by current
state-of-the-art approaches. Motivated by this fact, we propose a
knowledge-aware semantic parser to improve parsing performance by integrating
various types of knowledge. In this paper, we consider three types of
knowledge, including grammar knowledge, expert knowledge, and external resource
knowledge. First, grammar knowledge empowers the model to effectively replicate
previously generated logical form, which effectively handles the co-reference
and ellipsis phenomena in conversation Second, based on expert knowledge, we
propose a decomposable model, which is more controllable compared with
traditional end-to-end models that put all the burdens of learning on
trial-and-error in an end-to-end way. Third, external resource knowledge, i.e.,
provided by a pre-trained language model or an entity typing model, is used to
improve the representation of question and table for a better semantic
understanding. We conduct experiments on the SequentialQA dataset. Results show
that our knowledge-aware model outperforms the state-of-the-art approaches.
Incremental experimental results also prove the usefulness of various
knowledge. Further analysis shows that our approach has the ability to derive
the meaning representation of a context-dependent utterance by leveraging
previously generated outcomes
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
Few-Shot NLG with Pre-Trained Language Model
Neural-based end-to-end approaches to natural language generation (NLG) from
structured data or knowledge are data-hungry, making their adoption for
real-world applications difficult with limited data. In this work, we propose
the new task of \textit{few-shot natural language generation}. Motivated by how
humans tend to summarize tabular data, we propose a simple yet effective
approach and show that it not only demonstrates strong performance but also
provides good generalization across domains. The design of the model
architecture is based on two aspects: content selection from input data and
language modeling to compose coherent sentences, which can be acquired from
prior knowledge. With just 200 training examples, across multiple domains, we
show that our approach achieves very reasonable performances and outperforms
the strongest baseline by an average of over 8.0 BLEU points improvement. Our
code and data can be found at \url{https://github.com/czyssrs/Few-Shot-NLG}Comment: ACL 202
Learning Robust Dialog Policies in Noisy Environments
Modern virtual personal assistants provide a convenient interface for
completing daily tasks via voice commands. An important consideration for these
assistants is the ability to recover from automatic speech recognition (ASR)
and natural language understanding (NLU) errors. In this paper, we focus on
learning robust dialog policies to recover from these errors. To this end, we
develop a user simulator which interacts with the assistant through voice
commands in realistic scenarios with noisy audio, and use it to learn dialog
policies through deep reinforcement learning. We show that dialogs generated by
our simulator are indistinguishable from human generated dialogs, as determined
by human evaluators. Furthermore, preliminary experimental results show that
the learned policies in noisy environments achieve the same execution success
rate with fewer dialog turns compared to fixed rule-based policies.Comment: 1st Workshop on Conversational AI at NIPS 201
Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation
We conduct a large-scale, systematic study to evaluate the existing
evaluation methods for natural language generation in the context of generating
online product reviews. We compare human-based evaluators with a variety of
automated evaluation procedures, including discriminative evaluators that
measure how well machine-generated text can be distinguished from human-written
text, as well as word overlap metrics that assess how similar the generated
text compares to human-written references. We determine to what extent these
different evaluators agree on the ranking of a dozen of state-of-the-art
generators for online product reviews. We find that human evaluators do not
correlate well with discriminative evaluators, leaving a bigger question of
whether adversarial accuracy is the correct objective for natural language
generation. In general, distinguishing machine-generated text is challenging
even for human evaluators, and human decisions correlate better with lexical
overlaps. We find lexical diversity an intriguing metric that is indicative of
the assessments of different evaluators. A post-experiment survey of
participants provides insights into how to evaluate and improve the quality of
natural language generation systems
- ā¦