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Found 39 papers, 14 papers with code
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.
Policy-Driven Neural Response Generation for Knowledge-Grounded Dialog Systems
Open-domain dialog systems aim to generate relevant, informative and engaging responses.
"What do others think?": Task-Oriented Conversational Modeling with Subjective Knowledge
We hope this task and dataset can promote further research on TOD and subjective content understanding.
Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information
This work focuses on in-context data augmentation for intent detection.
Ranked #1 on
Intent Detection
on HWU64 5-shot
PLACES: Prompting Language Models for Social Conversation Synthesis
Collecting high quality conversational data can be very expensive for most applications and infeasible for others due to privacy, ethical, or similar concerns.
Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding
Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings.
Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks
Specifically, we show that for open-domain conversations with 10\% of seed data, our approach performs close to the baseline that uses 100% of the data, while for knowledge-grounded conversations, it achieves the same using only 1% of the data, on human ratings of engagingness, fluency, and relevance.
Towards Textual Out-of-Domain Detection without In-Domain Labels
In many real-world settings, machine learning models need to identify user inputs that are out-of-domain (OOD) so as to avoid performing wrong actions.
Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation
Implicit knowledge, such as common sense, is key to fluid human conversations.
Training Conversational Agents with Generative Conversational Networks
Rich, open-domain textual data available on the web resulted in great advancements for language processing.
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.
Towards Zero and Few-shot Knowledge-seeking Turn Detection in Task-orientated Dialogue Systems
Most prior work on task-oriented dialogue systems is restricted to supporting domain APIs.
Commonsense-Focused Dialogues for Response Generation: An Empirical Study
Moreover, existing dialogue datasets do not explicitly focus on exhibiting commonsense as a facet.
Can I Be of Further Assistance? Using Unstructured Knowledge Access to Improve Task-oriented Conversational Modeling
Most prior work on task-oriented dialogue systems are restricted to limited coverage of domain APIs.
Generative Conversational Networks
We show an average improvement of 35% in intent detection and 21% in slot tagging over a baseline model trained from the seed data.
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.
Video Question Answering on Screencast Tutorials
This paper presents a new video question answering task on screencast tutorials.
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.
TutorialVQA: Question Answering Dataset for Tutorial Videos
The results indicate that the task is challenging and call for the investigation of new algorithms.
Just Ask:An Interactive Learning Framework for Vision and Language Navigation
We demonstrate the proposed strategy is substantially more realistic and data-efficient compared to previously proposed pre-exploration techniques.
The Eighth Dialog System Technology Challenge
This paper introduces the Eighth Dialog System Technology Challenge.
Analyzing Sentence Fusion in Abstractive Summarization
While recent work in abstractive summarization has resulted in higher scores in automatic metrics, there is little understanding on how these systems combine information taken from multiple document sentences.
Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions
In visual communication, text emphasis is used to increase the comprehension of written text to convey the author{'}s intent.
Scoring Sentence Singletons and Pairs for Abstractive Summarization
There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs.
Neural Sentence Embedding using Only In-domain Sentences for Out-of-domain Sentence Detection in Dialog Systems
Then we used domain-category analysis as an auxiliary task to train neural sentence embedding for OOD sentence detection.
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
Neural abstractive summarization models have led to promising results in summarizing relatively short documents.
Ranked #4 on
Unsupervised Extractive Summarization
on Pubmed
Abstractive Text Summarization
Unsupervised Extractive Summarization
Truly Multi-modal YouTube-8M Video Classification with Video, Audio, and Text
Beyond that, we extend the original competition by including text information in the classification, making this a truly multi-modal approach with vision, audio and text.
