1,572 research outputs found
BERT with History Answer Embedding for Conversational Question Answering
Conversational search is an emerging topic in the information retrieval
community. One of the major challenges to multi-turn conversational search is
to model the conversation history to answer the current question. Existing
methods either prepend history turns to the current question or use complicated
attention mechanisms to model the history. We propose a conceptually simple yet
highly effective approach referred to as history answer embedding. It enables
seamless integration of conversation history into a conversational question
answering (ConvQA) model built on BERT (Bidirectional Encoder Representations
from Transformers). We first explain our view that ConvQA is a simplified but
concrete setting of conversational search, and then we provide a general
framework to solve ConvQA. We further demonstrate the effectiveness of our
approach under this framework. Finally, we analyze the impact of different
numbers of history turns under different settings to provide new insights into
conversation history modeling in ConvQA.Comment: Accepted to SIGIR 2019 as a short pape
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
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History Modeling for Conversational Information Retrieval
Conversational search is an embodiment of an iterative and interactive approach to information retrieval (IR) that has been studied for decades. Due to the recent rise of intelligent personal assistants, such as Siri, Alexa, AliMe, Cortana, and Google Assistant, a growing part of the population is moving their information-seeking activities to voice- or text-based conversational interfaces. One of the major challenges of conversational search is to leverage the conversation history to understand and fulfill the users\u27 information needs. In this dissertation work, we investigate history modeling approaches for conversational information retrieval. We start from history modeling for user intent prediction. We analyze information-seeking conversations by user intent distribution, co-occurrence, and flow patterns, followed by a study of user intent prediction in an information-seeking setting with both feature-based methods and deep learning methods. We then move to history modeling for conversational question answering (ConvQA), which can be considered as a simplified setting of conversational search. We first propose a positional history answer embedding (PosHAE) method to seamlessly integrate conversation history into a ConvQA model based on BERT. We then build upon this method and design a history attention mechanism (HAM) to conduct a ``soft selection\u27\u27 for conversation history. After this, we extend the previous ConvQA task to an open-retrieval (ORConvQA) setting to emphasize the fundamental role of retrieval in conversational search. In this setting, we learn to retrieve evidence from a large collection before extracting answers. We build an end-to-end system for ORConvQA, featuring a learnable dense retriever. We conduct experiments with both fully-supervised and weakly-supervised approaches to tackle the training challenges of ORConvQA. Finally, we study history modeling for conversational re-ranking. Given a history of user feedback behaviors, such as issuing a query, clicking a document, and skipping a document, we propose to introduce behavior awareness to a neural ranker. Our experimental results show that the history modeling approaches proposed in this dissertation can effectively improve the performance of different conversation tasks and provide new insights into conversational information retrieval
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