4,472 research outputs found
Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning
The goal of a Question Paraphrase Retrieval (QPR) system is to retrieve
equivalent questions that result in the same answer as the original question.
Such a system can be used to understand and answer rare and noisy
reformulations of common questions by mapping them to a set of canonical forms.
This has large-scale applications for community Question Answering (cQA) and
open-domain spoken language question answering systems. In this paper we
describe a new QPR system implemented as a Neural Information Retrieval (NIR)
system consisting of a neural network sentence encoder and an approximate
k-Nearest Neighbour index for efficient vector retrieval. We also describe our
mechanism to generate an annotated dataset for question paraphrase retrieval
experiments automatically from question-answer logs via distant supervision. We
show that the standard loss function in NIR, triplet loss, does not perform
well with noisy labels. We propose smoothed deep metric loss (SDML) and with
our experiments on two QPR datasets we show that it significantly outperforms
triplet loss in the noisy label setting
An Ensemble Model with Ranking for Social Dialogue
Open-domain social dialogue is one of the long-standing goals of Artificial
Intelligence. This year, the Amazon Alexa Prize challenge was announced for the
first time, where real customers get to rate systems developed by leading
universities worldwide. The aim of the challenge is to converse "coherently and
engagingly with humans on popular topics for 20 minutes". We describe our Alexa
Prize system (called 'Alana') consisting of an ensemble of bots, combining
rule-based and machine learning systems, and using a contextual ranking
mechanism to choose a system response. The ranker was trained on real user
feedback received during the competition, where we address the problem of how
to train on the noisy and sparse feedback obtained during the competition.Comment: NIPS 2017 Workshop on Conversational A
Commonsense Properties from Query Logs and Question Answering Forums
Commonsense knowledge about object properties, human behavior and general concepts is crucial for robust AI applications. However, automatic acquisition of this knowledge is challenging because of sparseness and bias in online sources. This paper presents Quasimodo, a methodology and tool suite for distilling commonsense properties from non-standard web sources. We devise novel ways of tapping into search-engine query logs and QA forums, and combining the resulting candidate assertions with statistical cues from encyclopedias, books and image tags in a corroboration step. Unlike prior work on commonsense knowledge bases, Quasimodo focuses on salient properties that are typically associated with certain objects or concepts. Extensive evaluations, including extrinsic use-case studies, show that Quasimodo provides better coverage than state-of-the-art baselines with comparable quality
Generating Synthetic Data for Neural Keyword-to-Question Models
Search typically relies on keyword queries, but these are often semantically
ambiguous. We propose to overcome this by offering users natural language
questions, based on their keyword queries, to disambiguate their intent. This
keyword-to-question task may be addressed using neural machine translation
techniques. Neural translation models, however, require massive amounts of
training data (keyword-question pairs), which is unavailable for this task. The
main idea of this paper is to generate large amounts of synthetic training data
from a small seed set of hand-labeled keyword-question pairs. Since natural
language questions are available in large quantities, we develop models to
automatically generate the corresponding keyword queries. Further, we introduce
various filtering mechanisms to ensure that synthetic training data is of high
quality. We demonstrate the feasibility of our approach using both automatic
and manual evaluation. This is an extended version of the article published
with the same title in the Proceedings of ICTIR'18.Comment: Extended version of ICTIR'18 full paper, 11 page
TiFi: Taxonomy Induction for Fictional Domains [Extended version]
Taxonomies are important building blocks of structured knowledge bases, and their construction from text sources and Wikipedia has received much attention. In this paper we focus on the construction of taxonomies for fictional domains, using noisy category systems from fan wikis or text extraction as input. Such fictional domains are archetypes of entity universes that are poorly covered by Wikipedia, such as also enterprise-specific knowledge bases or highly specialized verticals. Our fiction-targeted approach, called TiFi, consists of three phases: (i) category cleaning, by identifying candidate categories that truly represent classes in the domain of interest, (ii) edge cleaning, by selecting subcategory relationships that correspond to class subsumption, and (iii) top-level construction, by mapping classes onto a subset of high-level WordNet categories. A comprehensive evaluation shows that TiFi is able to construct taxonomies for a diverse range of fictional domains such as Lord of the Rings, The Simpsons or Greek Mythology with very high precision and that it outperforms state-of-the-art baselines for taxonomy induction by a substantial margin
Query Resolution for Conversational Search with Limited Supervision
In this work we focus on multi-turn passage retrieval as a crucial component
of conversational search. One of the key challenges in multi-turn passage
retrieval comes from the fact that the current turn query is often
underspecified due to zero anaphora, topic change, or topic return. Context
from the conversational history can be used to arrive at a better expression of
the current turn query, defined as the task of query resolution. In this paper,
we model the query resolution task as a binary term classification problem: for
each term appearing in the previous turns of the conversation decide whether to
add it to the current turn query or not. We propose QuReTeC (Query Resolution
by Term Classification), a neural query resolution model based on bidirectional
transformers. We propose a distant supervision method to automatically generate
training data by using query-passage relevance labels. Such labels are often
readily available in a collection either as human annotations or inferred from
user interactions. We show that QuReTeC outperforms state-of-the-art models,
and furthermore, that our distant supervision method can be used to
substantially reduce the amount of human-curated data required to train
QuReTeC. We incorporate QuReTeC in a multi-turn, multi-stage passage retrieval
architecture and demonstrate its effectiveness on the TREC CAsT dataset.Comment: SIGIR 2020 full conference pape
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