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DataFunTalk
DataFunTalk
Sep 27, 2022 · Artificial Intelligence

Contrastive Learning for Text Generation: Motivation, Methodology, Experiments, and Discussion (CoNT Framework)

This article reviews the integration of contrastive learning into text generation, explains why it helps mitigate exposure bias, introduces the CoNT framework with three key improvements, presents extensive experiments on translation, summarization, code comment and data‑to‑text tasks, and discusses practical deployment considerations.

AICoNTNatural Language Processing
0 likes · 21 min read
Contrastive Learning for Text Generation: Motivation, Methodology, Experiments, and Discussion (CoNT Framework)
DataFunTalk
DataFunTalk
Apr 6, 2021 · Artificial Intelligence

Advances in Text Summarization: Pointer-Generator, Coverage Mechanisms, Entity Knowledge Integration, and Non-Autoregressive Models

This article reviews recent advances in abstractive summarization, covering pointer‑generator networks with coverage loss, integration of entity knowledge, strategies to mitigate repetition such as unlikelihood training and nucleus sampling, and emerging non‑autoregressive approaches like the Levenshtein Transformer.

CoverageNLPentity-knowledge
0 likes · 15 min read
Advances in Text Summarization: Pointer-Generator, Coverage Mechanisms, Entity Knowledge Integration, and Non-Autoregressive Models