Improving Neural Language Models by Segmenting, Attending, and Predicting the Future (P19-1)
Copied to clipboard
| Challenge: | Common language models typically predict the next word given a past context. |
| Approach: | They propose a method that aligns the given context and the following phrase . they define syntactic heights and phrase segmentation rules to enable it to learn . |
| Outcome: | The proposed model outperforms strong baseline models on Wikitext-103 dataset. |
Similar Papers
Learning to Discover, Ground and Use Words with Segmental Neural Language Models (P19-1)
Copied to clipboard
| Challenge: | Existing models of word learning do not account for the long-range dependencies manifest in language and that are easily captured by recurrent neural networks. |
| Approach: | They propose a segmental neural language model that unifies word discovery, learning how words fit together to form sentences, and by conditioning the model on visual context, how words’ meanings ground in representations of nonlinguistic modalities. |
| Outcome: | The proposed model learns predictive distributions better than character LSTM models, discovers words competitively with nonparametric Bayesian word segmentation models, and improves on both. |
How to represent a word and predict it, too: Improving tied architectures for language modelling (D18-1)
Copied to clipboard
| Challenge: | Recent state-of-the-art models use word embeddings as input and output mappings instead of tied models. |
| Approach: | They propose to decouple hidden state from word embedding prediction . they extend their proposed modification to word2vec models . |
| Outcome: | The proposed architectures achieve comparable or better results compared to previous models without tying . the proposed architecture reduces parameters, enabling more compact models and faster learning. |
Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach (2020.acl-main)
Copied to clipboard
| Challenge: | incorporating syntactic structure into language models has been a challenge since the 1990s. |
| Approach: | They propose to use syntactic information to integrate syntastic structure into neural language models by providing ground truth parse trees as additional training signals. |
| Outcome: | The proposed model achieves lower perplexity and better quality when ground truth parse trees are provided as training signals. |
Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing models of language understanding are based on explicit representations of hierarchical structure, but there are good reasons to doubt that they can be said to understand language in any meaningful way. |
| Approach: | They examine whether syntactic and semantic graph representations can complement and improve neural language modeling. |
| Outcome: | The proposed model outperforms pretrained models on English WSJ in perplexity and other metrics. |
Contextual String Embeddings for Sequence Labeling (C18-1)
Copied to clipboard
| Challenge: | Recent advances in language modeling have made it viable to model language as distributions over characters. |
| Approach: | They propose to leverage internal states of a trained character language model to produce a new type of word embeddings. |
| Outcome: | The proposed embeddings outperform the state-of-the-art on four classic sequence labeling tasks. |
Predicting Reference: What do Language Models Learn about Discourse Models? (2020.emnlp-main)
Copied to clipboard
| Challenge: | a growing literature that probes neural language models to assess their latent acquisition of grammatical knowledge has not investigated their acquisition of discourse modeling ability. |
| Approach: | They draw on a psycholinguistic literature that has established how different contexts affect referential biases concerning who is likely to be referred to next. |
| Outcome: | The proposed models do not resemble human language users, the authors show . their models capture the linguistic knowledge required to perform discourse modeling . |
Third-Party Aligner for Neural Word Alignments (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work shows that word alignment can be competitive . |
| Approach: | They propose to use word alignments generated by a third-party word aligner to supervise the neural word alignment training. |
| Outcome: | The proposed approach can find more accurate word alignments and delete wrong alignments, leading to better performance than the current best third-party word aligner. |
Language models and brains align due to more than next-word prediction and word-level information (2024.emnlp-main)
Copied to clipboard
| Challenge: | Pretrained language models have been shown to significantly predict brain recordings of people comprehending language. |
| Approach: | They propose to use two perturbations to design contrasts that control for different types of information. |
| Outcome: | The proposed model is largely agnostic about the exact linguistic information contained in the conceptual quantities "word-level information" and "multi-word information". |
Probabilistic Bilingual Subword Segmentation with Latent Subword Alignment (2026.eacl-srw)
Copied to clipboard
| Challenge: | Existing methods do not consider parallel relationships, preventing translation model training. |
| Approach: | They propose a method for learning subword correspondences in parallel sentence pairs using the EM algorithm. |
| Outcome: | The proposed method improves translation accuracy for many tasks. |
Toward Better Storylines with Sentence-Level Language Models (2020.acl-main)
Copied to clipboard
| Challenge: | Rather than modeling fluency, the sentence-level language model can focus on longer range dependencies, which are crucial for multi-sentence coherence. |
| Approach: | They propose a sentence-level language model which selects the next sentence in a story from a finite set of fluent alternatives. |
| Outcome: | The proposed model can focus on longer range dependencies, crucial for multi-sentence coherence. |