8,951 research outputs found
A Hybrid Retrieval-Generation Neural Conversation Model
Intelligent personal assistant systems that are able to have multi-turn
conversations with human users are becoming increasingly popular. Most previous
research has been focused on using either retrieval-based or generation-based
methods to develop such systems. Retrieval-based methods have the advantage of
returning fluent and informative responses with great diversity. However, the
performance of the methods is limited by the size of the response repository.
On the other hand, generation-based methods can produce highly coherent
responses on any topics. But the generated responses are often generic and not
informative due to the lack of grounding knowledge. In this paper, we propose a
hybrid neural conversation model that combines the merits of both response
retrieval and generation methods. Experimental results on Twitter and
Foursquare data show that the proposed model outperforms both retrieval-based
methods and generation-based methods (including a recently proposed
knowledge-grounded neural conversation model) under both automatic evaluation
metrics and human evaluation. We hope that the findings in this study provide
new insights on how to integrate text retrieval and text generation models for
building conversation systems.Comment: Accepted as a Full Paper in CIKM 2019. 10 page
RubyStar: A Non-Task-Oriented Mixture Model Dialog System
RubyStar is a dialog system designed to create "human-like" conversation by
combining different response generation strategies. RubyStar conducts a
non-task-oriented conversation on general topics by using an ensemble of
rule-based, retrieval-based and generative methods. Topic detection, engagement
monitoring, and context tracking are used for managing interaction. Predictable
elements of conversation, such as the bot's backstory and simple question
answering are handled by separate modules. We describe a rating scheme we
developed for evaluating response generation. We find that character-level RNN
is an effective generation model for general responses, with proper parameter
settings; however other kinds of conversation topics might benefit from using
other models
A Dual Encoder Sequence to Sequence Model for Open-Domain Dialogue Modeling
Ever since the successful application of sequence to sequence learning for
neural machine translation systems, interest has surged in its applicability
towards language generation in other problem domains. Recent work has
investigated the use of these neural architectures towards modeling open-domain
conversational dialogue, where it has been found that although these models are
capable of learning a good distributional language model, dialogue coherence is
still of concern. Unlike translation, conversation is much more a one-to-many
mapping from utterance to a response, and it is even more pressing that the
model be aware of the preceding flow of conversation. In this paper we propose
to tackle this problem by introducing previous conversational context in terms
of latent representations of dialogue acts over time. We inject the latent
context representations into a sequence to sequence neural network in the form
of dialog acts using a second encoder to enhance the quality and the coherence
of the conversations generated. The main task of this research work is to show
that adding latent variables that capture discourse relations does indeed
result in more coherent responses when compared to conventional sequence to
sequence models
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
Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence
The neural attention model has achieved great success in data-to-text
generation tasks. Though usually excelling at producing fluent text, it suffers
from the problem of information missing, repetition and "hallucination". Due to
the black-box nature of the neural attention architecture, avoiding these
problems in a systematic way is non-trivial. To address this concern, we
propose to explicitly segment target text into fragment units and align them
with their data correspondences. The segmentation and correspondence are
jointly learned as latent variables without any human annotations. We further
impose a soft statistical constraint to regularize the segmental granularity.
The resulting architecture maintains the same expressive power as neural
attention models, while being able to generate fully interpretable outputs with
several times less computational cost. On both E2E and WebNLG benchmarks, we
show the proposed model consistently outperforms its neural attention
counterparts.Comment: Accepted at ACL 202
Sentence Pair Scoring: Towards Unified Framework for Text Comprehension
We review the task of Sentence Pair Scoring, popular in the literature in
various forms - viewed as Answer Sentence Selection, Semantic Text Scoring,
Next Utterance Ranking, Recognizing Textual Entailment, Paraphrasing or e.g. a
component of Memory Networks.
We argue that all such tasks are similar from the model perspective and
propose new baselines by comparing the performance of common IR metrics and
popular convolutional, recurrent and attention-based neural models across many
Sentence Pair Scoring tasks and datasets. We discuss the problem of evaluating
randomized models, propose a statistically grounded methodology, and attempt to
improve comparisons by releasing new datasets that are much harder than some of
the currently used well explored benchmarks. We introduce a unified open source
software framework with easily pluggable models and tasks, which enables us to
experiment with multi-task reusability of trained sentence model. We set a new
state-of-art in performance on the Ubuntu Dialogue dataset.Comment: submitted as paper to CoNLL 201
Joint Learning of Sentence Embeddings for Relevance and Entailment
We consider the problem of Recognizing Textual Entailment within an
Information Retrieval context, where we must simultaneously determine the
relevancy as well as degree of entailment for individual pieces of evidence to
determine a yes/no answer to a binary natural language question.
We compare several variants of neural networks for sentence embeddings in a
setting of decision-making based on evidence of varying relevance. We propose a
basic model to integrate evidence for entailment, show that joint training of
the sentence embeddings to model relevance and entailment is feasible even with
no explicit per-evidence supervision, and show the importance of evaluating
strong baselines. We also demonstrate the benefit of carrying over text
comprehension model trained on an unrelated task for our small datasets.
Our research is motivated primarily by a new open dataset we introduce,
consisting of binary questions and news-based evidence snippets. We also apply
the proposed relevance-entailment model on a similar task of ranking
multiple-choice test answers, evaluating it on a preliminary dataset of school
test questions as well as the standard MCTest dataset, where we improve the
neural model state-of-art.Comment: repl4nlp workshop at ACL Berlin 201
The RLLChatbot: a solution to the ConvAI challenge
Current conversational systems can follow simple commands and answer basic
questions, but they have difficulty maintaining coherent and open-ended
conversations about specific topics. Competitions like the Conversational
Intelligence (ConvAI) challenge are being organized to push the research
development towards that goal. This article presents in detail the RLLChatbot
that participated in the 2017 ConvAI challenge. The goal of this research is to
better understand how current deep learning and reinforcement learning tools
can be used to build a robust yet flexible open domain conversational agent. We
provide a thorough description of how a dialog system can be built and trained
from mostly public-domain datasets using an ensemble model. The first
contribution of this work is a detailed description and analysis of different
text generation models in addition to novel message ranking and selection
methods. Moreover, a new open-source conversational dataset is presented.
Training on this data significantly improves the Recall@k score of the ranking
and selection mechanisms compared to our baseline model responsible for
selecting the message returned at each interaction.Comment: 46 pages including references and appendix, 14 figures, 12 tables;
Under review for the Dialogue & Discourse journa
Towards Exploiting Background Knowledge for Building Conversation Systems
Existing dialog datasets contain a sequence of utterances and responses
without any explicit background knowledge associated with them. This has
resulted in the development of models which treat conversation as a
sequence-to-sequence generation task i.e, given a sequence of utterances
generate the response sequence). This is not only an overly simplistic view of
conversation but it is also emphatically different from the way humans converse
by heavily relying on their background knowledge about the topic (as opposed to
simply relying on the previous sequence of utterances). For example, it is
common for humans to (involuntarily) produce utterances which are copied or
suitably modified from background articles they have read about the topic. To
facilitate the development of such natural conversation models which mimic the
human process of conversing, we create a new dataset containing movie chats
wherein each response is explicitly generated by copying and/or modifying
sentences from unstructured background knowledge such as plots, comments and
reviews about the movie. We establish baseline results on this dataset (90K
utterances from 9K conversations) using three different models: (i) pure
generation based models which ignore the background knowledge (ii) generation
based models which learn to copy information from the background knowledge when
required and (iii) span prediction based models which predict the appropriate
response span in the background knowledge.Comment: Camera Ready EMNLP 201
Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems
Task-oriented conversational modeling with unstructured knowledge access, as
track 1 of the 9th Dialogue System Technology Challenges (DSTC 9), requests to
build a system to generate response given dialogue history and knowledge
access. This challenge can be separated into three subtasks, (1)
knowledge-seeking turn detection, (2) knowledge selection, and (3)
knowledge-grounded response generation. We use pre-trained language models,
ELECTRA and RoBERTa, as our base encoder for different subtasks. For subtask 1
and 2, the coarse-grained information like domain and entity are used to
enhance knowledge usage. For subtask 3, we use a latent variable to encode
dialog history and selected knowledge better and generate responses combined
with copy mechanism. Meanwhile, some useful post-processing strategies are
performed on the model's final output to make further knowledge usage in the
generation task. As shown in released evaluation results, our proposed system
ranks second under objective metrics and ranks fourth under human metrics.Comment: Accepted by AAAI 2021, Workshop on DSTC
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