SLICE: Supersense-based Lightweight Interpretable Contextual Embeddings (2020.coling-main)
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| Challenge: | Contextualised embeddings are a key component of human languages but their opaqueness makes it difficult to interpret their behaviour. |
| Approach: | They propose a weakly supervised method to learn interpretable embeddings from raw corpora and seed words. |
| Outcome: | The proposed model can represent both a word and its context as embeddings into the same compact space, whose dimensions correspond to interpretable supersenses. |
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Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations (D19-60)
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| Challenge: | Pretrained deep contextual representations have advanced the state-of-the-art on various commonsense NLP tasks, but we lack a concrete understanding of their capabilities. |
| Approach: | They investigate BERT's ability to encode various commonsense features in its embedding space, but are still deficient in many areas. |
| Outcome: | The proposed model improves performance on a downstream commonsense reasoning task while using minimal data. |
Contextualized Embeddings for Enriching Linguistic Analyses on Politeness (2020.coling-main)
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| Challenge: | Current word embeddings in natural language processing do capture context and thus can be leveraged to enrich linguistic analyses. |
| Approach: | They propose a model which leverages pre-trained BERT to cluster contextualized representations of a word based on context in which it appears and labels of items it occurs in. |
| Outcome: | The proposed model can detect interpretable, finer-grained context patterns associated with (im)polite language. |
Using Paraphrases to Study Properties of Contextual Embeddings (2022.naacl-main)
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| Challenge: | Previously, paraphrases have been used to probe whether compositionality is accurately captured by BERT, but we believe they can be used to explore many other questions. |
| Approach: | They propose to use paraphrases as a unique source of data to analyze contextualized embeddings, with a particular focus on BERT. |
| Outcome: | The proposed analysis of paraphrases and paraphrase representations using the Paraphrase Database shows that BERT handles polysemous words, but different representations in many cases. |
BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance (2020.acl-main)
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| Challenge: | Existing approaches to improve word embeddings for rare words are limited to uncontextualized word embeds. |
| Approach: | They propose a powerful architecture that can infer high-quality embeddings for rare words . they use the surface form and contexts of a word to interact in a deep architecture . |
| Outcome: | The proposed architecture can infer high-quality embeddings for rare words that are suitable as input representations for deep language models. |
Contextual Embeddings: When Are They Worth It? (2020.acl-main)
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| Challenge: | In recent years, rich contextual embeddings have enabled rapid progress on benchmarks like GLUE, but require significant computational resources during pretraining and during downstream task training and inference. |
| Approach: | They empirically compare contextual embeddings with classic pretrained embedders and a random word embeddable with a simple baseline. |
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iBERT: Interpretable Embeddings via Sense Decomposition (2026.eacl-long)
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| Challenge: | iBERT is an encoder that produces inherently interpretable and controllable embeddings without compromising performance. |
| Approach: | They propose an encoder that produces interpretable embeddings that modularize and expose discriminative cues present in language. |
| Outcome: | The proposed model outperforms baselines on style-focused tasks while maintaining competitive performance on authorship verification. |
Word2Sense: Sparse Interpretable Word Embeddings (P19-1)
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| Challenge: | Word2Sense embeddings are interpretable, but they are sparse and fast to compute . a unitary rotation can be applied to many of these embeddables retaining their utility for computational tasks while changing the values of individual coordinates. |
| Approach: | They propose an unsupervised method to generate Word2Sense word embeddings that are interpretable. |
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Spying on Your Neighbors: Fine-grained Probing of Contextual Embeddings for Information about Surrounding Words (2020.acl-main)
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| Challenge: | a suite of probing tasks test contextual embeddings for encoding of information about surrounding words . authors: little is known about what information embeddables encode about the context words encode . a recent study shows that contextual embeds can be powerful for many tasks . |
| Approach: | They propose probing tasks that enable fine-grained testing of contextual embeddings . they examine popular contextual encoders and find that each encodes contextual information across tokens a little different . |
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Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation (P19-1)
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| Challenge: | Pretrained contextual and non-contextual subword embeddings are available in over 250 languages, allowing massively multilingual NLP. |
| Approach: | They compare pretrained contextual and non-contextual subword embeddings with a contextual representation method, namely BERT, on multilingual named entity recognition and part-of-speech tagging. |
| Outcome: | The proposed method outperforms non-contextual embeddings on multilingual named entity recognition and part-of-speech tagging. |
BERTrade: Using Contextual Embeddings to Parse Old French (2022.lrec-1)
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| Challenge: | a growing interest in digital humanities for automatic processing and annotation of historical texts is generating new models for historical languages. |
| Approach: | They use POS-tagging and dependency parsing to evaluate contextual word embedding models . Old French is one of the historical languages for which they have the largest amount of syntactically annotated data . |
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