2,273 research outputs found
A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems
Search-oriented conversational systems rely on information needs expressed in
natural language (NL). We focus here on the understanding of NL expressions for
building keyword-based queries. We propose a reinforcement-learning-driven
translation model framework able to 1) learn the translation from NL
expressions to queries in a supervised way, and, 2) to overcome the lack of
large-scale dataset by framing the translation model as a word selection
approach and injecting relevance feedback in the learning process. Experiments
are carried out on two TREC datasets and outline the effectiveness of our
approach.Comment: This is the author's pre-print version of the work. It is posted here
for your personal use, not for redistribution. Please cite the definitive
version which will be published in Proceedings of the 2018 EMNLP Workshop
SCAI: The 2nd International Workshop on Search-Oriented Conversational AI -
ISBN: 978-1-948087-75-
Learning to Attend, Copy, and Generate for Session-Based Query Suggestion
Users try to articulate their complex information needs during search
sessions by reformulating their queries. To make this process more effective,
search engines provide related queries to help users in specifying the
information need in their search process. In this paper, we propose a
customized sequence-to-sequence model for session-based query suggestion. In
our model, we employ a query-aware attention mechanism to capture the structure
of the session context. is enables us to control the scope of the session from
which we infer the suggested next query, which helps not only handle the noisy
data but also automatically detect session boundaries. Furthermore, we observe
that, based on the user query reformulation behavior, within a single session a
large portion of query terms is retained from the previously submitted queries
and consists of mostly infrequent or unseen terms that are usually not included
in the vocabulary. We therefore empower the decoder of our model to access the
source words from the session context during decoding by incorporating a copy
mechanism. Moreover, we propose evaluation metrics to assess the quality of the
generative models for query suggestion. We conduct an extensive set of
experiments and analysis. e results suggest that our model outperforms the
baselines both in terms of the generating queries and scoring candidate queries
for the task of query suggestion.Comment: Accepted to be published at The 26th ACM International Conference on
Information and Knowledge Management (CIKM2017
Task-Oriented Query Reformulation with Reinforcement Learning
Search engines play an important role in our everyday lives by assisting us
in finding the information we need. When we input a complex query, however,
results are often far from satisfactory. In this work, we introduce a query
reformulation system based on a neural network that rewrites a query to
maximize the number of relevant documents returned. We train this neural
network with reinforcement learning. The actions correspond to selecting terms
to build a reformulated query, and the reward is the document recall. We
evaluate our approach on three datasets against strong baselines and show a
relative improvement of 5-20% in terms of recall. Furthermore, we present a
simple method to estimate a conservative upper-bound performance of a model in
a particular environment and verify that there is still large room for
improvements.Comment: EMNLP 201
Towards Exploratory Reformulation of Constraint Models
It is well established that formulating an effective constraint model of a
problem of interest is crucial to the efficiency with which it can subsequently
be solved. Following from the observation that it is difficult, if not
impossible, to know a priori which of a set of candidate models will perform
best in practice, we envisage a system that explores the space of models
through a process of reformulation from an initial model, guided by performance
on a set of training instances from the problem class under consideration. We
plan to situate this system in a refinement-based approach, where a user writes
a constraint specification describing a problem above the level of abstraction
at which many modelling decisions are made. In this position paper we set out
our plan for an exploratory reformulation system, and discuss progress made so
far.Comment: 13 pages, 6 figure
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