Papers with out-of-vocabulary words
PBoS: Probabilistic Bag-of-Subwords for Generalizing Word Embedding (2020.findings-emnlp)
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| Challenge: | Existing word embeddings assume fixed finite-size vocabularies, hindering their ability to provide useful word representations for out-of-vocaulary words. |
| Approach: | They propose a model that generalizes word embeddings without extra contextual information . they use the spellings of words to model subword segmentation and compute subword-based compositional word embeds. |
| Outcome: | The proposed model can generate meaningful subword segmentations without any source of explicit morphological knowledge. |
A Better Way to Do Masked Language Model Scoring (2023.acl-short)
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| Challenge: | Existing methods to estimate log-likelihood of sentences are not available for masked language models . et al., 2020: e.on.org/sci/sql/src/spll-word-l2r estimates sentence pseudo-log-likellihoods for a given sentence under autoregressive models are difficult to perform on controlled benchmarks. |
| Approach: | They propose to estimate sentence pseudo-log-likelihood scores by successively masking each sentence token, retrieving its score using the rest of the sentence as context, and summing the resulting values. |
| Outcome: | The proposed method outperforms both the original and the adapted method on masked language models. |
An Empirical Study of the Downstream Reliability of Pre-Trained Word Embeddings (2020.coling-main)
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| Challenge: | Pre-trained word embeddings have been shown to improve the performance of neural networks across a wide variety of tasks. |
| Approach: | They propose two new metrics to understand the downstream reliability of word embeddings. |
| Outcome: | The proposed model can improve performance with slight changes to the training data, but it can also fail with multiple neural network architectures. |
Evaluating Pixel Language Models on Non-Standardized Languages (2025.coling-main)
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| Challenge: | pixel-based models can be used to transfer learning from standard languages to dialects . pretrained language models achieve strong results for languages seen during training, but their performance declines with out-of-domain dialects. |
| Approach: | They compare pixel-based models to token-based ones to evaluate dialects . standard german is tokenized in a more meaningful way, whereas the Bern dialect is tokenize in pixel form . |
| Outcome: | The proposed models outperform token-based models in part-of-speech tagging, dependency parsing and intent detection for zero-shot dialect evaluation by up to 26 percentage points in some scenarios, though not in Standard German. |