Papers with n-gram language models
Decipherment of Substitution Ciphers with Neural Language Models (D18-1)
Copied to clipboard
| Challenge: | Existing methods for deciphering homophonic substitution ciphers use pre-trained neural LMs. |
| Approach: | They propose a beam search algorithm that scores the entire candidate plaintext at each step of the decipherment using a neural language model. |
| Outcome: | The proposed beam search algorithm improves on challenging ciphers with smaller beam sizes and better error rates than state-of-the-art methods. |
JBLiMP: Japanese Benchmark of Linguistic Minimal Pairs (2023.findings-eacl)
Copied to clipboard
| Challenge: | In this paper, we compare syntactic knowledge of language models across different languages. |
| Approach: | They introduce a dataset for targeted syntactic evaluations of language models in Japanese. |
| Outcome: | The proposed dataset compares the syntactic knowledge of language models across languages. |
Can Transformers Learn n-gram Language Models? (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing work has tested transformers' ability to represent formal languages, but language models are not classifiers of strings but rather distributions over them. |
| Approach: | They relate transformers' ability to learn random n-gram language models to ngram language model (LM) they find add- smoothing outperforms transformers on the former, while transformers perform better on the latter . |
| Outcome: | The proposed models outperform classical methods designed to learn n-gram LMs, while transformers perform better on the latter. |
Good-Enough Compositional Data Augmentation (2020.acl-main)
Copied to clipboard
| Challenge: | a proposed data augmentation protocol provides a compositional inductive bias in conditional and unconditional sequence models. |
| Approach: | They propose a data augmentation protocol that provides a compositional inductive bias in conditional and unconditional sequence models by replacing discontinuous fragments with other fragments that appear in at least one similar environment. |
| Outcome: | The proposed protocol reduces error rate by 87% on diagnostic tasks and 16% on semantic parsing tasks. |
Are Decoder-Only Language Models Better than Encoder-Only Language Models in Understanding Word Meaning? (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models are highly effective tools for solving different kinds of problems in natural language processing. |
| Approach: | They propose to use large language models to solve a myriad of problems. |
| Outcome: | The proposed model performs worse on word meaning comprehension than an encoder-only model with vastly fewer parameters. |