11,500 research outputs found
Attentive History Selection for Conversational Question Answering
Conversational question answering (ConvQA) is a simplified but concrete
setting of conversational search. One of its major challenges is to leverage
the conversation history to understand and answer the current question. In this
work, we propose a novel solution for ConvQA that involves three aspects.
First, we propose a positional history answer embedding method to encode
conversation history with position information using BERT in a natural way.
BERT is a powerful technique for text representation. Second, we design a
history attention mechanism (HAM) to conduct a "soft selection" for
conversation histories. This method attends to history turns with different
weights based on how helpful they are on answering the current question. Third,
in addition to handling conversation history, we take advantage of multi-task
learning (MTL) to do answer prediction along with another essential
conversation task (dialog act prediction) using a uniform model architecture.
MTL is able to learn more expressive and generic representations to improve the
performance of ConvQA. We demonstrate the effectiveness of our model with
extensive experimental evaluations on QuAC, a large-scale ConvQA dataset. We
show that position information plays an important role in conversation history
modeling. We also visualize the history attention and provide new insights into
conversation history understanding.Comment: Accepted to CIKM 201
Open-Retrieval Conversational Question Answering
Conversational search is one of the ultimate goals of information retrieval.
Recent research approaches conversational search by simplified settings of
response ranking and conversational question answering, where an answer is
either selected from a given candidate set or extracted from a given passage.
These simplifications neglect the fundamental role of retrieval in
conversational search. To address this limitation, we introduce an
open-retrieval conversational question answering (ORConvQA) setting, where we
learn to retrieve evidence from a large collection before extracting answers,
as a further step towards building functional conversational search systems. We
create a dataset, OR-QuAC, to facilitate research on ORConvQA. We build an
end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader
that are all based on Transformers. Our extensive experiments on OR-QuAC
demonstrate that a learnable retriever is crucial for ORConvQA. We further show
that our system can make a substantial improvement when we enable history
modeling in all system components. Moreover, we show that the reranker
component contributes to the model performance by providing a regularization
effect. Finally, further in-depth analyses are performed to provide new
insights into ORConvQA.Comment: Accepted to SIGIR'2
Learning to Select the Relevant History Turns in Conversational Question Answering
The increasing demand for the web-based digital assistants has given a rapid
rise in the interest of the Information Retrieval (IR) community towards the
field of conversational question answering (ConvQA). However, one of the
critical aspects of ConvQA is the effective selection of conversational history
turns to answer the question at hand. The dependency between relevant history
selection and correct answer prediction is an intriguing but under-explored
area. The selected relevant context can better guide the system so as to where
exactly in the passage to look for an answer. Irrelevant context, on the other
hand, brings noise to the system, thereby resulting in a decline in the model's
performance. In this paper, we propose a framework, DHS-ConvQA (Dynamic History
Selection in Conversational Question Answering), that first generates the
context and question entities for all the history turns, which are then pruned
on the basis of similarity they share in common with the question at hand. We
also propose an attention-based mechanism to re-rank the pruned terms based on
their calculated weights of how useful they are in answering the question. In
the end, we further aid the model by highlighting the terms in the re-ranked
conversational history using a binary classification task and keeping the
useful terms (predicted as 1) and ignoring the irrelevant terms (predicted as
0). We demonstrate the efficacy of our proposed framework with extensive
experimental results on CANARD and QuAC -- the two popularly utilized datasets
in ConvQA. We demonstrate that selecting relevant turns works better than
rewriting the original question. We also investigate how adding the irrelevant
history turns negatively impacts the model's performance and discuss the
research challenges that demand more attention from the IR community
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
Conversational Machine Comprehension: a Literature Review
Conversational Machine Comprehension (CMC), a research track in
conversational AI, expects the machine to understand an open-domain natural
language text and thereafter engage in a multi-turn conversation to answer
questions related to the text. While most of the research in Machine Reading
Comprehension (MRC) revolves around single-turn question answering (QA),
multi-turn CMC has recently gained prominence, thanks to the advancement in
natural language understanding via neural language models such as BERT and the
introduction of large-scale conversational datasets such as CoQA and QuAC. The
rise in interest has, however, led to a flurry of concurrent publications, each
with a different yet structurally similar modeling approach and an inconsistent
view of the surrounding literature. With the volume of model submissions to
conversational datasets increasing every year, there exists a need to
consolidate the scattered knowledge in this domain to streamline future
research. This literature review attempts at providing a holistic overview of
CMC with an emphasis on the common trends across recently published models,
specifically in their approach to tackling conversational history. The review
synthesizes a generic framework for CMC models while highlighting the
differences in recent approaches and intends to serve as a compendium of CMC
for future researchers.Comment: Accepted to COLING 202
- …