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Found 13 papers, 4 papers with code
An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation
Different from conventional text generation tasks, the mapping between inputs and responses in conversations is more complicated, which highly demands the understanding of utterance-level semantic dependency, a relation between the whole meanings of inputs and outputs.
Ranked #1 on
Text Generation
on DailyDialog
Learning Personalized End-to-End Goal-Oriented Dialog
Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues.
Text Assisted Insight Ranking Using Context-Aware Memory Network
Extracting valuable facts or informative summaries from multi-dimensional tables, i. e. insight mining, is an important task in data analysis and business intelligence.
Adaptive Gradient Methods with Dynamic Bound of Learning Rate
Recent work has put forward some algorithms such as AMSGrad to tackle this issue but they failed to achieve considerable improvement over existing methods.
MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning
In this work, we explore parallel multi-scale representation learning on sequence data, striving to capture both long-range and short-range language structures.
Ranked #8 on
Machine Translation
on WMT2014 English-French
Large-Scale Generative Data-Free Distillation
Knowledge distillation is one of the most popular and effective techniques for knowledge transfer, model compression and semi-supervised learning.
Bridging the Gap Between Object Detection and User Intent via Query-Modulation
When interacting with objects through cameras, or pictures, users often have a specific intent.
RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting
In this work, we develop new strategies for instruction tuning and reinforcement learning to better align LLMs for cross-sentence rewriting tasks using diverse wording and structures expressed through natural languages including 1) generating rewriting instruction data from Wiki edits and public corpus through instruction generation and chain-of-thought prompting; 2) collecting comparison data for reward model training through a new ranking function.
Towards an On-device Agent for Text Rewriting
Large Language Models (LLMs) have demonstrated impressive capabilities for text rewriting.
Critique Ability of Large Language Models
In the era of large language models (LLMs), this study explores the ability of LLMs to deliver accurate critiques across various tasks.
Fusion-Eval: Integrating Evaluators with LLMs
Evaluating natural language systems poses significant challenges, particularly in the realms of natural language understanding and high-level reasoning.
SiRA: Sparse Mixture of Low Rank Adaptation
Parameter Efficient Tuning has been an prominent approach to adapt the Large Language Model to downstream tasks.
Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision
Process supervision, using a trained verifier to evaluate the intermediate steps generated by reasoner, has demonstrated significant improvements in multi-step problem solving.
