36,515 research outputs found
Recent advances in conversational NLP : Towards the standardization of Chatbot building
Dialogue systems have become recently essential in our life. Their use is
getting more and more fluid and easy throughout the time. This boils down to
the improvements made in NLP and AI fields. In this paper, we try to provide an
overview to the current state of the art of dialogue systems, their categories
and the different approaches to build them. We end up with a discussion that
compares all the techniques and analyzes the strengths and weaknesses of each.
Finally, we present an opinion piece suggesting to orientate the research
towards the standardization of dialogue systems building.Comment: 8 pages with references, 1 figur
Cross-domain Dialogue Policy Transfer via Simultaneous Speech-act and Slot Alignment
Dialogue policy transfer enables us to build dialogue policies in a target
domain with little data by leveraging knowledge from a source domain with
plenty of data. Dialogue sentences are usually represented by speech-acts and
domain slots, and the dialogue policy transfer is usually achieved by assigning
a slot mapping matrix based on human heuristics. However, existing dialogue
policy transfer methods cannot transfer across dialogue domains with different
speech-acts, for example, between systems built by different companies. Also,
they depend on either common slots or slot entropy, which are not available
when the source and target slots are totally disjoint and no database is
available to calculate the slot entropy. To solve this problem, we propose a
Policy tRansfer across dOMaIns and SpEech-acts (PROMISE) model, which is able
to transfer dialogue policies across domains with different speech-acts and
disjoint slots. The PROMISE model can learn to align different speech-acts and
slots simultaneously, and it does not require common slots or the calculation
of the slot entropy. Experiments on both real-world dialogue data and
simulations demonstrate that PROMISE model can effectively transfer dialogue
policies across domains with different speech-acts and disjoint slots.Comment: v
A Survey on Dialog Management: Recent Advances and Challenges
Dialog management (DM) is a crucial component in a task-oriented dialog
system. Given the dialog history, DM predicts the dialog state and decides the
next action that the dialog agent should take. Recently, dialog policy learning
has been widely formulated as a Reinforcement Learning (RL) problem, and more
works focus on the applicability of DM. In this paper, we survey recent
advances and challenges within three critical topics for DM: (1) improving
model scalability to facilitate dialog system modeling in new scenarios, (2)
dealing with the data scarcity problem for dialog policy learning, and (3)
enhancing the training efficiency to achieve better task-completion performance
. We believe that this survey can shed a light on future research in dialog
management
A Deep Reinforcement Learning Chatbot
We present MILABOT: a deep reinforcement learning chatbot developed by the
Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize
competition. MILABOT is capable of conversing with humans on popular small talk
topics through both speech and text. The system consists of an ensemble of
natural language generation and retrieval models, including template-based
models, bag-of-words models, sequence-to-sequence neural network and latent
variable neural network models. By applying reinforcement learning to
crowdsourced data and real-world user interactions, the system has been trained
to select an appropriate response from the models in its ensemble. The system
has been evaluated through A/B testing with real-world users, where it
performed significantly better than many competing systems. Due to its machine
learning architecture, the system is likely to improve with additional data.Comment: 40 pages, 9 figures, 11 table
Decoupling Strategy and Generation in Negotiation Dialogues
We consider negotiation settings in which two agents use natural language to
bargain on goods. Agents need to decide on both high-level strategy (e.g.,
proposing \$50) and the execution of that strategy (e.g., generating "The bike
is brand new. Selling for just \$50."). Recent work on negotiation trains
neural models, but their end-to-end nature makes it hard to control their
strategy, and reinforcement learning tends to lead to degenerate solutions. In
this paper, we propose a modular approach based on coarse di- alogue acts
(e.g., propose(price=50)) that decouples strategy and generation. We show that
we can flexibly set the strategy using supervised learning, reinforcement
learning, or domain-specific knowledge without degeneracy, while our
retrieval-based generation can maintain context-awareness and produce diverse
utterances. We test our approach on the recently proposed DEALORNODEAL game,
and we also collect a richer dataset based on real items on Craigslist. Human
evaluation shows that our systems achieve higher task success rate and more
human-like negotiation behavior than previous approaches.Comment: EMNLP 201
Latent Intention Dialogue Models
Developing a dialogue agent that is capable of making autonomous decisions
and communicating by natural language is one of the long-term goals of machine
learning research. Traditional approaches either rely on hand-crafting a small
state-action set for applying reinforcement learning that is not scalable or
constructing deterministic models for learning dialogue sentences that fail to
capture natural conversational variability. In this paper, we propose a Latent
Intention Dialogue Model (LIDM) that employs a discrete latent variable to
learn underlying dialogue intentions in the framework of neural variational
inference. In a goal-oriented dialogue scenario, these latent intentions can be
interpreted as actions guiding the generation of machine responses, which can
be further refined autonomously by reinforcement learning. The experimental
evaluation of LIDM shows that the model out-performs published benchmarks for
both corpus-based and human evaluation, demonstrating the effectiveness of
discrete latent variable models for learning goal-oriented dialogues.Comment: Accepted at ICML 201
An Adversarial Approach to High-Quality, Sentiment-Controlled Neural Dialogue Generation
In this work, we propose a method for neural dialogue response generation
that allows not only generating semantically reasonable responses according to
the dialogue history, but also explicitly controlling the sentiment of the
response via sentiment labels. Our proposed model is based on the paradigm of
conditional adversarial learning; the training of a sentiment-controlled
dialogue generator is assisted by an adversarial discriminator which assesses
the fluency and feasibility of the response generating from the dialogue
history and a given sentiment label. Because of the flexibility of our
framework, the generator could be a standard sequence-to-sequence (SEQ2SEQ)
model or a more complicated one such as a conditional variational
autoencoder-based SEQ2SEQ model. Experimental results using automatic and human
evaluation both demonstrate that our proposed framework is able to generate
both semantically reasonable and sentiment-controlled dialogue responses.Comment: DEEP-DIAL 201
Chat as Expected: Learning to Manipulate Black-box Neural Dialogue Models
Recently, neural network based dialogue systems have become ubiquitous in our
increasingly digitalized society. However, due to their inherent opaqueness,
some recently raised concerns about using neural models are starting to be
taken seriously. In fact, intentional or unintentional behaviors could lead to
a dialogue system to generate inappropriate responses. Thus, in this paper, we
investigate whether we can learn to craft input sentences that result in a
black-box neural dialogue model being manipulated into having its outputs
contain target words or match target sentences. We propose a reinforcement
learning based model that can generate such desired inputs automatically.
Extensive experiments on a popular well-trained state-of-the-art neural
dialogue model show that our method can successfully seek out desired inputs
that lead to the target outputs in a considerable portion of cases.
Consequently, our work reveals the potential of neural dialogue models to be
manipulated, which inspires and opens the door towards developing strategies to
defend them.Comment: 10 page
ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems
We present ConvLab-2, an open-source toolkit that enables researchers to
build task-oriented dialogue systems with state-of-the-art models, perform an
end-to-end evaluation, and diagnose the weakness of systems. As the successor
of ConvLab (Lee et al., 2019b), ConvLab-2 inherits ConvLab's framework but
integrates more powerful dialogue models and supports more datasets. Besides,
we have developed an analysis tool and an interactive tool to assist
researchers in diagnosing dialogue systems. The analysis tool presents rich
statistics and summarizes common mistakes from simulated dialogues, which
facilitates error analysis and system improvement. The interactive tool
provides a user interface that allows developers to diagnose an assembled
dialogue system by interacting with the system and modifying the output of each
system component.Comment: Accepted by ACL 2020 demo trac
Learning how to learn: an adaptive dialogue agent for incrementally learning visually grounded word meanings
We present an optimised multi-modal dialogue agent for interactive learning
of visually grounded word meanings from a human tutor, trained on real
human-human tutoring data. Within a life-long interactive learning period, the
agent, trained using Reinforcement Learning (RL), must be able to handle
natural conversations with human users and achieve good learning performance
(accuracy) while minimising human effort in the learning process. We train and
evaluate this system in interaction with a simulated human tutor, which is
built on the BURCHAK corpus -- a Human-Human Dialogue dataset for the visual
learning task. The results show that: 1) The learned policy can coherently
interact with the simulated user to achieve the goal of the task (i.e. learning
visual attributes of objects, e.g. colour and shape); and 2) it finds a better
trade-off between classifier accuracy and tutoring costs than hand-crafted
rule-based policies, including ones with dynamic policies.Comment: 10 pages, RoboNLP Workshop from ACL Conferenc
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