27,505 research outputs found
Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion
Fact-centric information needs are rarely one-shot; users typically ask follow-up questions to explore a topic. In such a conversational setting, the user's inputs are often incomplete, with entities or predicates left out, and ungrammatical phrases. This poses a huge challenge to question answering (QA) systems that typically rely on cues in full-fledged interrogative sentences. As a solution, we develop CONVEX: an unsupervised method that can answer incomplete questions over a knowledge graph (KG) by maintaining conversation context using entities and predicates seen so far and automatically inferring missing or ambiguous pieces for follow-up questions. The core of our method is a graph exploration algorithm that judiciously expands a frontier to find candidate answers for the current question. To evaluate CONVEX, we release ConvQuestions, a crowdsourced benchmark with 11,200 distinct conversations from five different domains. We show that CONVEX: (i) adds conversational support to any stand-alone QA system, and (ii) outperforms state-of-the-art baselines and question completion strategies
Interpretation of Natural Language Rules in Conversational Machine Reading
Most work in machine reading focuses on question answering problems where the
answer is directly expressed in the text to read. However, many real-world
question answering problems require the reading of text not because it contains
the literal answer, but because it contains a recipe to derive an answer
together with the reader's background knowledge. One example is the task of
interpreting regulations to answer "Can I...?" or "Do I have to...?" questions
such as "I am working in Canada. Do I have to carry on paying UK National
Insurance?" after reading a UK government website about this topic. This task
requires both the interpretation of rules and the application of background
knowledge. It is further complicated due to the fact that, in practice, most
questions are underspecified, and a human assistant will regularly have to ask
clarification questions such as "How long have you been working abroad?" when
the answer cannot be directly derived from the question and text. In this
paper, we formalise this task and develop a crowd-sourcing strategy to collect
32k task instances based on real-world rules and crowd-generated questions and
scenarios. We analyse the challenges of this task and assess its difficulty by
evaluating the performance of rule-based and machine-learning baselines. We
observe promising results when no background knowledge is necessary, and
substantial room for improvement whenever background knowledge is needed.Comment: EMNLP 201
Feasibility report: Delivering case-study based learning using artificial intelligence and gaming technologies
This document describes an investigation into the technical feasibility of a game to support learning based on case studies. Information systems students using the game will conduct fact-finding interviews with virtual characters. We survey relevant technologies in computational linguistics and games. We assess the applicability of the various approaches and propose an architecture for the game based on existing techniques. We propose a phased development plan for the development of the game
TAPAS: Weakly Supervised Table Parsing via Pre-training
Answering natural language questions over tables is usually seen as a
semantic parsing task. To alleviate the collection cost of full logical forms,
one popular approach focuses on weak supervision consisting of denotations
instead of logical forms. However, training semantic parsers from weak
supervision poses difficulties, and in addition, the generated logical forms
are only used as an intermediate step prior to retrieving the denotation. In
this paper, we present TAPAS, an approach to question answering over tables
without generating logical forms. TAPAS trains from weak supervision, and
predicts the denotation by selecting table cells and optionally applying a
corresponding aggregation operator to such selection. TAPAS extends BERT's
architecture to encode tables as input, initializes from an effective joint
pre-training of text segments and tables crawled from Wikipedia, and is trained
end-to-end. We experiment with three different semantic parsing datasets, and
find that TAPAS outperforms or rivals semantic parsing models by improving
state-of-the-art accuracy on SQA from 55.1 to 67.2 and performing on par with
the state-of-the-art on WIKISQL and WIKITQ, but with a simpler model
architecture. We additionally find that transfer learning, which is trivial in
our setting, from WIKISQL to WIKITQ, yields 48.7 accuracy, 4.2 points above the
state-of-the-art.Comment: Accepted to ACL 202
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