A variety of contextualised language models have been proposed in the NLP
community, which are trained on diverse corpora to produce numerous Neural
Language Models (NLMs). However, different NLMs have reported different levels
of performances in downstream NLP applications when used as text
representations. We propose a sentence-level meta-embedding learning method
that takes independently trained contextualised word embedding models and
learns a sentence embedding that preserves the complementary strengths of the
input source NLMs. Our proposed method is unsupervised and is not tied to a
particular downstream task, which makes the learnt meta-embeddings in principle
applicable to different tasks that require sentence representations.
Specifically, we first project the token-level embeddings obtained by the
individual NLMs and learn attention weights that indicate the contributions of
source embeddings towards their token-level meta-embeddings. Next, we apply
mean and max pooling to produce sentence-level meta-embeddings from token-level
meta-embeddings. Experimental results on semantic textual similarity benchmarks
show that our proposed unsupervised sentence-level meta-embedding method
outperforms previously proposed sentence-level meta-embedding methods as well
as a supervised baseline.

提出了一种句子级元嵌入学习方法，利用不同的上下文化语言模型，通过无监督的学习方式学习句子嵌入，以满足不同需要的下游自然语言处理任务，实验证明提出的方法优于以往的方法和有监督的基线。

基于注意力机制的非监督句级元嵌入来自上下文语言模型

Unsupervised Attention-based Sentence-Level Meta-Embeddings from Contextualised Language Models

One of the central aspects of contextualised language models is that they
should be able to distinguish the meaning of lexically ambiguous words by their
contexts. In this paper we investigate the extent to which the contextualised
embeddings of word forms that display multiplicity of sense reflect traditional
distinctions of polysemy and homonymy. To this end, we introduce an extended,
human-annotated dataset of graded word sense similarity and co-predication
acceptability, and evaluate how well the similarity of embeddings predicts
similarity in meaning. Both types of human judgements indicate that the
similarity of polysemic interpretations falls in a continuum between identity
of meaning and homonymy. However, we also observe significant differences
within the similarity ratings of polysemes, forming consistent patterns for
different types of polysemic sense alternation. Our dataset thus appears to
capture a substantial part of the complexity of lexical ambiguity, and can
provide a realistic test bed for contextualised embeddings. Among the tested
models, BERT Large shows the strongest correlation with the collected word
sense similarity ratings, but struggles to consistently replicate the observed
similarity patterns. When clustering ambiguous word forms based on their
embeddings, the model displays high confidence in discerning homonyms and some
types of polysemic alternations, but consistently fails for others.

本文研究了语境化语言模型的多义词辨别能力。作者提出了一个人工注释的数据集，用于评估多义词词义的相似性，发现多义词的词义相似性介于相同和同音异义之间，并呈现出一定的词义变化规律性。BERT Large 在相似性等级方面表现最佳，但无法一致地复制观察到的相似性模式，并且在某些类型的多义词变化上存在问题。