Response Generation
370 papers with code • 3 benchmarks • 8 datasets
A task where an agent should play the $DE$ role and generate a text to respond to a $P$ message.
Libraries
Use these libraries to find Response Generation models and implementations
SIMMC2.0
ProsocialDialog
KETOD
ComFact
MMConv
LLMafia
Comet
ArgSciChat
Most implemented papers
Language Models are Unsupervised Multitask Learners
openai/gpt-2
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Preprint 2019
Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.
A Diversity-Promoting Objective Function for Neural Conversation Models
pender/chatbot-rnn
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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.
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
Sequential data often possesses a hierarchical structure with complex dependencies between subsequences, such as found between the utterances in a dialogue.
Unified Language Model Pre-training for Natural Language Understanding and Generation
microsoft/unilm
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NeurIPS 2019
This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks.
MASS: Masked Sequence to Sequence Pre-training for Language Generation
microsoft/MASS
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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.
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
inimah/metric-preference-checklist
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Our proposed framework provides access: (i) for verifying whether automatic metrics are faithful to human preference, regardless of their correlation level to human; and (ii) for inspecting the strengths and limitations of NLG systems via pairwise evaluation.
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer).
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Our framework trains a single arbitrary LM that adaptively retrieves passages on-demand, and generates and reflects on retrieved passages and its own generations using special tokens, called reflection tokens.
MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling
Even though machine learning has become the major scene in dialogue research community, the real breakthrough has been blocked by the scale of data available.
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language tokens.
