Conversational Response Generation

10 papers with code • 0 benchmarks • 0 datasets

Given an input conversation, generate a natural-looking text reply to the last conversation element.

Image credit: DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation

Datasets


Greatest papers with code

DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation

huggingface/pytorch-transformers 1 Nov 2019

We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer).

Conversational Response Generation

DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation

microsoft/DialoGPT ACL 2020

We present a large, tunable neural conversational response generation model, DIALOGPT (dialogue generative pre-trained transformer).

Conversational Response Generation

MASS: Masked Sequence to Sequence Pre-training for Language Generation

microsoft/MASS 7 May 2019

Pre-training and fine-tuning, e. g., BERT, have achieved great success in language understanding by transferring knowledge from rich-resource pre-training task to the low/zero-resource downstream tasks.

Conversational Response Generation Text Generation +2

A Diversity-Promoting Objective Function for Neural Conversation Models

pender/chatbot-rnn NAACL 2016

Sequence-to-sequence neural network models for generation of conversational responses tend to generate safe, commonplace responses (e. g., "I don't know") regardless of the input.

Conversational Response Generation

PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation

alibaba/AliceMind 14 Apr 2020

An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues.

Abstractive Text Summarization Conversational Response Generation +6

Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization

xcfcode/PLM_annotator 26 May 2021

Current dialogue summarization systems usually encode the text with a number of general semantic features (e. g., keywords and topics) to gain more powerful dialogue modeling capabilities.

Conversational Response Generation Language Modelling

Learning to Abstract for Memory-augmented Conversational Response Generation

tianzhiliang/MemoryAugDialog ACL 2019

In this work, we propose a memory-augmented generative model, which learns to abstract from the training corpus and saves the useful information to the memory to assist the response generation.

Conversational Response Generation

Conversations with Search Engines: SERP-based Conversational Response Generation

PengjieRen/CaSE-1.0 29 Apr 2020

In this paper, we address the problem of answering complex information needs by conversing conversations with search engines, in the sense that users can express their queries in natural language, and directly receivethe information they need from a short system response in a conversational manner.

Conversational Response Generation Conversational Search +1

RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models

umanlp/RedditBias 7 Jun 2021

We use the evaluation framework to benchmark the widely used conversational DialoGPT model along with the adaptations of four debiasing methods.

Conversational Response Generation