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An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractGenerating paraphrases from given sentences involves decoding words step by step from a large vocabulary. To learn a decoder, supervised learning which maximizes the likelihood of tokens always suffers from …...

8899

Autoregressive Knowledge Distillation through Imitation Learning

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractThe performance of autoregressive models on natural language generation tasks has dramatically improved due to the adoption of deep, self-attentive architectures. However, these gains have come at the cost of …...

30090

Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractKnowledge selection plays an important role in knowledge-grounded dialogue, which is a challenging task to generate more informative responses by leveraging external knowledge. Recently, latent variable models have been proposed …...

29680

Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractJoint entity and relation extraction aims to extract relation triplets from plain text directly. Prior work leverages Sequence-to-Sequence (Seq2Seq) models for triplet sequence generation. However, Seq2Seq enforces an unnecessary order …...

32454

On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractThe standard training algorithm in neural machine translation (NMT) suffers from exposure bias, and alternative algorithms have been proposed to mitigate this. However, the practical impact of exposure bias is …...

37789

Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation?

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractExposure bias has been regarded as a central problem for auto-regressive language models (LM). It claims that teacher forcing would cause the test-time generation to be incrementally distorted due to …...

31688

TILGAN: Transformer-based Implicit Latent GAN for Diverse and Coherent Text Generation

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

Conventional autoregressive models have achieved great success in text generation but suffer from the exposure bias problem in that token sequences in the training and in the gen- eration stages …...

33756

Type-Driven Multi-Turn Corrections for Grammatical Error Correction

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractGrammatical Error Correction (GEC) aims to automatically detect and correct grammatical errors. In this aspect, dominant models are trained by one-iteration learning while performing multiple iterations of corrections during inference. …...

32547

Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractCurrent language generation models suffer from issues such as repetition, incoherence, and hallucinations. An often-repeated hypothesis for this brittleness of generation models is that it is caused by the training …...

32907

Understanding and Bridging the Modality Gap for Speech Translation

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractHow to achieve better end-to-end speech translation (ST) by leveraging (text) machine translation (MT) data? Among various existing techniques, multi-task learning is one of the effective ways to share knowledge …...

37536