This repository provides pre-trained encoder-decoder models and its related optimization techniques developed by Alibaba's MinD (Machine IntelligeNce of Damo) Lab.

The family of AliceMind:

• Language understanding model: StructBERT (ICLR 2020)
• Generative language model: PALM (EMNLP 2020)
• Cross-lingual language model: VECO (ACL 2021)
• Cross-modal language model: StructVBERT (CVPR 2020 VQA Challenge Runner-up)
• Structural language model: StructuralLM (ACL 2021)
• Chinese language understanding model with multi-granularity inputs: LatticeBERT (NAACL 2021)

• March, 2021: AliceMind released!
• May, 2021: VECO and StructuralLM were accepted by ACL 2021.

• StructBERT (March 15, 2021): pre-trained models for natural language understanding (NLU). We extend BERT to a new model, StructBERT, by incorporating language structures into pre-training. Specifically, we pre-train StructBERT with two auxiliary tasks to make the most of the sequential order of words and sentences, which leverage language structures at the word and sentence levels, respectively. "StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding" (ICLR 2020)

• PALM (March 15, 2021): pre-trained models for natural language generation (NLG). We propose 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. It achieves new SOTA results in several downstream tasks. "PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation" (EMNLP 2020)

• VECO v0 (March 15, 2021): pre-trained models for cross-lingual (x) natural language understanding (x-NLU) and generation (x-NLG). VECO (v0) achieves the new SOTA results on various cross-lingual understanding tasks of the XTREME benchmark, covering text classification, sequence labeling, question answering, and sentence retrieval. For cross-lingual generation tasks, it also outperforms all existing cross-lingual models and state-of-the-art Transformer variants on WMT14 English-to-German and English-to-French translation datasets, with gains of up to 1~2 BLEU. “VECO: Variable Encoder-decoder Pre-training for Cross-lingual Understanding and Generation" (ACL 2021)

• StructVBERT (March 15, 2021): pre-trained models for vision-language understanding. We propose a new single-stream visual-linguistic pre-training scheme by leveraging multi-stage progressive pre-training and multi-task learning. StructVBERT obtained the 2020 VQA Challenge Runner-up award, and SOTA result on VQA 2020 public Test-standard benchmark (June 2020). "Talk Slides" (CVPR 2020 VQA Challenge Runner-up).

• StructuralLM (March 15, 2021): pre-trained models for document-image understanding. We propose a new pre-training approach, StructuralLM, to jointly leverage cell and layout information from scanned documents. The pre-trained StructuralLM achieves new state-of-the-art results in different types of downstream tasks. "StructuralLM: Structural Pre-training for Form Understanding" (ACL 2021)

• LatticeBERT (March 15, 2021): we propose a novel pre-training paradigm for Chinese — Lattice-BERT which explicitly incorporates word representations with those of characters, thus can model a sentence in a multi-granularity manner. "Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models" (NAACL 2021)

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AliceMind is released under the Apache 2.0 license.

Copyright 1999-2020 Alibaba Group Holding Ltd.

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