23,567 research outputs found
Conversational QA Dataset Generation with Answer Revision
Conversational question--answer generation is a task that automatically
generates a large-scale conversational question answering dataset based on
input passages. In this paper, we introduce a novel framework that extracts
question-worthy phrases from a passage and then generates corresponding
questions considering previous conversations. In particular, our framework
revises the extracted answers after generating questions so that answers
exactly match paired questions. Experimental results show that our simple
answer revision approach leads to significant improvement in the quality of
synthetic data. Moreover, we prove that our framework can be effectively
utilized for domain adaptation of conversational question answering.Comment: COLING 202
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
A Personalized System for Conversational Recommendations
Searching for and making decisions about information is becoming increasingly
difficult as the amount of information and number of choices increases.
Recommendation systems help users find items of interest of a particular type,
such as movies or restaurants, but are still somewhat awkward to use. Our
solution is to take advantage of the complementary strengths of personalized
recommendation systems and dialogue systems, creating personalized aides. We
present a system -- the Adaptive Place Advisor -- that treats item selection as
an interactive, conversational process, with the program inquiring about item
attributes and the user responding. Individual, long-term user preferences are
unobtrusively obtained in the course of normal recommendation dialogues and
used to direct future conversations with the same user. We present a novel user
model that influences both item search and the questions asked during a
conversation. We demonstrate the effectiveness of our system in significantly
reducing the time and number of interactions required to find a satisfactory
item, as compared to a control group of users interacting with a non-adaptive
version of the system
Enactivism and Robotic Language Acquisition: A Report from the Frontier
In this article, I assess an existing language acquisition architecture, which was deployed in linguistically unconstrained human–robot interaction, together with experimental design decisions with regard to their enactivist credentials. Despite initial scepticism with respect to enactivism’s applicability to the social domain, the introduction of the notion of participatory sense-making in the more recent enactive literature extends the framework’s reach to encompass this domain. With some exceptions, both our architecture and form of experimentation appear to be largely compatible with enactivist tenets. I analyse the architecture and design decisions along the five enactivist core themes of autonomy, embodiment, emergence, sense-making, and experience, and discuss the role of affect due to its central role within our acquisition experiments. In conclusion, I join some enactivists in demanding that interaction is taken seriously as an irreducible and independent subject of scientific investigation, and go further by hypothesising its potential value to machine learning.Peer reviewedFinal Published versio
Data Augmentation for Conversational AI
Advancements in conversational systems have revolutionized information
access, surpassing the limitations of single queries. However, developing
dialogue systems requires a large amount of training data, which is a challenge
in low-resource domains and languages. Traditional data collection methods like
crowd-sourcing are labor-intensive and time-consuming, making them ineffective
in this context. Data augmentation (DA) is an affective approach to alleviate
the data scarcity problem in conversational systems. This tutorial provides a
comprehensive and up-to-date overview of DA approaches in the context of
conversational systems. It highlights recent advances in conversation
augmentation, open domain and task-oriented conversation generation, and
different paradigms of evaluating these models. We also discuss current
challenges and future directions in order to help researchers and practitioners
to further advance the field in this area
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