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

TL;DR

PALM introduces a novel pre-training scheme combining autoencoding and autoregressive objectives, specifically designed to improve context-conditioned text generation tasks, achieving state-of-the-art results across multiple benchmarks.

Contribution

The paper proposes a new pre-training approach that jointly optimizes autoencoding and autoregressive objectives to better align with language generation tasks.

Findings

Achieves state-of-the-art on MARCO question answering

Sets new records in CNN/DailyMail summarization

Outperforms previous models in question generation and conversational response tasks

Abstract

Self-supervised pre-training, such as BERT, MASS and BART, has emerged as a powerful technique for natural language understanding and generation. Existing pre-training techniques employ autoencoding and/or autoregressive objectives to train Transformer-based models by recovering original word tokens from corrupted text with some masked tokens. The training goals of existing techniques are often inconsistent with the goals of many language generation tasks, such as generative question answering and conversational response generation, for producing new text given context. This work presents PALM with a novel scheme that jointly pre-trains an autoencoding and autoregressive language model on a large unlabeled corpus, specifically designed for generating new text conditioned on context. The new scheme alleviates the mismatch introduced by the existing denoising scheme between pre-training and fine-tuning where generation is more than reconstructing original text. 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.