Category: Nature Language Tech

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Introducing MetaICL: A Language Model Meta-Training Framework for Few-Shot In-Context Learning

A research team from the University of Washington, Facebook AI Research and the Allen Institute for AI introduces Meta-training for InContext Learning (MetaICL), a new meta-training framework for few-shot learning where an LM is meta-trained to learn in-context — conditioning on training examples to recover the task and make predictions.

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Mention Memory: Incorporating Factual Knowledge From Various Sources Into Transformers Without Supervision

A research team from the University of Southern California and Google proposes TOME, a “mention memory” approach to factual knowledge extraction for NLU tasks. A transformer model with attention over a semi-parametric representation of the entire Wikipedia text corpus, TOME can extract information without supervision and achieves strong performance on multiple open-domain question answering benchmarks.

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NYU & UNC Reveal How Transformers’ Learned Representations Change After Fine-Tuning

In the paper Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers, a research team from New York University and the University of North Carolina at Chapel Hill uses centered kernel alignment (CKA) to measure the similarity of representations across layers and explore how fine-tuning changes transformers’ learned representations.

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MIT’s Automatic Data-Driven Media Bias Measurement Method Achieves Human-Level Results

MIT researchers present an automated, objective and transparent data-driven method for measuring media bias. The study analyses roughly a million articles from about a hundred newspapers for bias on various news topics, maps the newspapers into a two-dimensional media bias landscape, and shows that the data-driven results agree well with human-judgement classifications.

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Apple Neural TTS System Study: Combining Speakers of Multiple Languages to Improve Synthetic Voice Quality

An Apple research team explores multiple architectures and training procedures to develop a novel multi-speaker and multi-lingual neural TTS system. The study combines speech from 30 speakers from 15 locales in 8 languages, and demonstrates that for the vast majority of voices, such multi-lingual and multi-speaker models can yield better quality than single speaker models.

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Google Researchers Enable Transformers to Solve Compositional NLP Tasks

A Google Research team explores the design space of Transformer models in an effort to enable deep learning architectures to solve compositional tasks. The proposed approach provides models with inductive biases via design decisions that significantly impact compositional generalization, and achieves state-of-the-art results on the COGS and PCFG composition benchmarks.

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Google’s H-Transformer-1D: Fast One-Dimensional Hierarchical Attention With Linear Complexity for Long Sequence Processing

A Google Research team draws inspiration from two numerical analysis methods — Hierarchical Matrix (H-Matrix) and Multigrid — to address the quadratic complexity problem of attention mechanisms in transformer architectures, proposing a hierarchical attention scheme that has linear complexity in run time and memory.

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Melbourne U, Facebook & Twitter Expose Novel Numerical Errors in NMT Systems

A research team from the University of Melbourne, Facebook AI, and Twitter Cortex proposes a black-box test method for assessing and debugging the numerical translation of neural machine translation systems in a systematic manner. The approach reveals novel types of errors that are general across multiple state-of-the-art translation systems.

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Google Researchers Merge Pretrained Teacher LMs Into a Single Multilingual Student LM Via Knowledge Distillation

A Google Research team proposes MergeDistill, a framework for merging pretrained teacher LMs from multiple monolingual/multilingual LMs into a single multilingual task-agnostic student LM to leverage the capabilities of the powerful language-specific LMs while still being multilingual and enabling positive language transfer.

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Study Shows Transformers Possess the Compositionality Power for Mathematical Reasoning

A research team from UC Davis, Microsoft Research and Johns Hopkins University extends work on training massive amounts of linguistic data to reveal the grammatical structures in their representations to the domain of mathematical reasoning, showing that both the standard transformer and the TP-Transformer can compose the meanings of mathematical symbols based on their structured relationships.