Search Results for author:
Found 25 papers, 8 papers with code
Policy-Driven Neural Response Generation for Knowledge-Grounded Dialog Systems
Open-domain dialog systems aim to generate relevant, informative and engaging responses.
Think Before You Speak: Learning to Generate Implicit Knowledge for Response Generation by Self-Talk
We further investigate can such models identify when to generate implicit background knowledge and when it is not necessary.
What is wrong with you?: Leveraging User Sentiment for Automatic Dialog Evaluation
Existing model-based metrics for system response evaluation are trained on human annotated data, which is cumbersome to collect.
Multi-Sentence Knowledge Selection in Open-Domain Dialogue
Incorporating external knowledge sources effectively in conversations is a longstanding problem in open-domain dialogue research.
User Response and Sentiment Prediction for Automatic Dialogue Evaluation
Automatic evaluation is beneficial for open-domain dialog system development.
Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation
Implicit knowledge, such as common sense, is key to fluid human conversations.
Rome was built in 1776: A Case Study on Factual Correctness in Knowledge-Grounded Response Generation
We demonstrate the benefit of our Conv-FEVER dataset by showing that the models trained on this data perform reasonably well to detect factually inconsistent responses with respect to the provided knowledge through evaluation on our human annotated data.
"How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations
Most prior work in dialogue modeling has been on written conversations mostly because of existing data sets.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
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Commonsense-Focused Dialogues for Response Generation: An Empirical Study
Moreover, existing dialogue datasets do not explicitly focus on exhibiting commonsense as a facet.
Go Beyond Plain Fine-tuning: Improving Pretrained Models for Social Commonsense
Towards improving language models' social intelligence, we focus on the Social IQA dataset, a task requiring social and emotional commonsense reasoning.
Incorporating Commonsense Knowledge Graph in Pretrained Models for Social Commonsense Tasks
Pretrained language models have excelled at many NLP tasks recently; however, their social intelligence is still unsatisfactory.
Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access Track in DSTC9
This challenge track aims to expand the coverage of task-oriented dialogue systems by incorporating external unstructured knowledge sources.
Overview of the Ninth Dialog System Technology Challenge: DSTC9
Interactive evaluation of dialog, and 4.
Are Neural Open-Domain Dialog Systems Robust to Speech Recognition Errors in the Dialog History? An Empirical Study
Large end-to-end neural open-domain chatbots are becoming increasingly popular.
Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access
In this paper, we propose to expand coverage of task-oriented dialogue systems by incorporating external unstructured knowledge sources.
Policy-Driven Neural Response Generation for Knowledge-Grounded Dialogue Systems
In this paper, we propose using a dialogue policy to plan the content and style of target responses in the form of an action plan, which includes knowledge sentences related to the dialogue context, targeted dialogue acts, topic information, etc.
Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations
We introduce Topical-Chat, a knowledge-grounded human-human conversation dataset where the underlying knowledge spans 8 broad topics and conversation partners don’t have explicitly defined roles, to help further research in open-domain conversational AI.
Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators
Having explicit feedback on the relevance and interestingness of a system response at each turn can be a useful signal for mitigating such issues and improving system quality by selecting responses from different approaches.
Natural Language Generation at Scale: A Case Study for Open Domain Question Answering
Our experiments show the feasibility of learning statistical NLG models for open-domain QA with larger ontologies.
Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize
In the second iteration of the competition in 2018, university teams advanced the state of the art by using context in dialog models, leveraging knowledge graphs for language understanding, handling complex utterances, building statistical and hierarchical dialog managers, and leveraging model-driven signals from user responses.
Detecting Offensive Content in Open-domain Conversations using Two Stage Semi-supervision
We train models using publicly available annotated datasets as well as using the proposed large-scale semi-supervised datasets.
Contextual Topic Modeling For Dialog Systems
On annotated data, we show that incorporating context and dialog acts leads to relative gains in topic classification accuracy by 35% and on unsupervised keyword detection recall by 11% for conversational interactions where topics frequently span multiple utterances.
Contextual Language Model Adaptation for Conversational Agents
Statistical language models (LM) play a key role in Automatic Speech Recognition (ASR) systems used by conversational agents.
Conversational AI: The Science Behind the Alexa Prize
This paper outlines the advances created by the university teams as well as the Alexa Prize team to achieve the common goal of solving the problem of Conversational
On Evaluating and Comparing Open Domain Dialog Systems
In this paper, we propose a comprehensive evaluation strategy with multiple metrics designed to reduce subjectivity by selecting metrics which correlate well with human judgement.
