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Found 18 papers, 8 papers with code
Integrating Question Rewrites in Conversational Question Answering: A Reinforcement Learning Approach
Resolving dependencies among dialogue history is one of the main obstacles in the research on conversational question answering (QA).
CAiRE in DialDoc21: Data Augmentation for Information Seeking Dialogue System
Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on users’ needs, which.
High-Dimension Human Value Representation in Large Language Models
there is an urgent need to understand the scope and nature of human values injected into these models before their release.
Contrastive Learning for Inference in Dialogue
Inference, especially those derived from inductive processes, is a crucial component in our conversation to complement the information implicitly or explicitly conveyed by a speaker.
Towards Mitigating Hallucination in Large Language Models via Self-Reflection
Large language models (LLMs) have shown promise for generative and knowledge-intensive tasks including question-answering (QA) tasks.
Can Question Rewriting Help Conversational Question Answering?
Question rewriting (QR) is a subtask of conversational question answering (CQA) aiming to ease the challenges of understanding dependencies among dialogue history by reformulating questions in a self-contained form.
VScript: Controllable Script Generation with Visual Presentation
In order to offer a customized script tool and inspire professional scriptwriters, we present VScript.
Survey of Hallucination in Natural Language Generation
This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation.
Greenformer: Factorization Toolkit for Efficient Deep Neural Networks
While the recent advances in deep neural networks (DNN) bring remarkable success, the computational cost also increases considerably.
Assessing Political Prudence of Open-domain Chatbots
In this work, as a first step towards a politically safe chatbot, we propose a group of metrics for assessing their political prudence.
CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System
Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on users' needs, which.
ERICA: An Empathetic Android Companion for Covid-19 Quarantine
Over the past year, research in various domains, including Natural Language Processing (NLP), has been accelerated to fight against the COVID-19 pandemic, yet such research has just started on dialogue systems.
Nora: The Well-Being Coach
The current pandemic has forced people globally to remain in isolation and practice social distancing, which creates the need for a system to combat the resulting loneliness and negative emotions.
Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters
This paper proposes KnowExpert, a framework to bypass the explicit retrieval process and inject knowledge into the pre-trained language models with lightweight adapters and adapt to the knowledge-grounded dialogue task.
Model Generalization on COVID-19 Fake News Detection
Amid the pandemic COVID-19, the world is facing unprecedented infodemic with the proliferation of both fake and real information.
Plug-and-Play Conversational Models
These large conversational models provide little control over the generated responses, and this control is further limited in the absence of annotated conversational datasets for attribute specific generation that can be used for fine-tuning the model.
Image Position Prediction in Multimodal Documents
Datasets for these tasks contain a large number of pairs of an image and the corresponding sentence as an instance.
XPersona: Evaluating Multilingual Personalized Chatbot
Experimental results show that the multilingual trained models outperform the translation-pipeline and that they are on par with the monolingual models, with the advantage of having a single model across multiple languages.
