| Challenge: | Current approaches to cross-lingual sentence encoders use sentence-level objectives only. |
| Approach: | They propose a novel approach that integrates both sentence-level and token-level objectives. |
| Outcome: | The proposed approach outperforms existing CLSEs on bitext mining tasks and downstream tasks. |
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Dual-Alignment Pre-training for Cross-lingual Sentence Embedding (2023.acl-long)
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Ziheng Li, Shaohan Huang, Zihan Zhang, Zhi-Hong Deng, Qiang Lou, Haizhen Huang, Jian Jiao, Furu Wei, Weiwei Deng, Qi Zhang
| Challenge: | Recent studies have shown that dual encoder models trained with the sentence-level translation ranking task are effective methods for cross-lingual sentence embedding. |
| Approach: | They propose a dual-alignment pre-training framework that incorporates both sentence-level and token-level alignment. |
| Outcome: | The proposed framework improves cross-lingual sentence embedding on three cross-linguistic benchmarks. |
Learn Your Tokens: Word-Pooled Tokenization for Language Modeling (2023.findings-emnlp)
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| Challenge: | Language models typically tokenize text into subwords, using a deterministic, hand-engineered heuristic of combining characters into longer surface-level strings such as ‘ing’ or whole words. |
| Approach: | They propose a 'learn your tokens' scheme which pooles bytes/characters into word representations and decodes individual characters/bytes per word in parallel. |
| Outcome: | The proposed tokenizer outperforms subword models and byte/character models over the word boundary and outperformed on rare words by a factor of 30! |
What is the best recipe for character-level encoder-only modelling? (2023.acl-long)
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| Challenge: | aims to benchmark recent progress in language understanding models that output contextualised representations at the character level. |
| Approach: | They aim to find the best way to build and train character-level BERT-like models by comparing architectural innovations with pretraining objectives. |
| Outcome: | The proposed model outperforms a token-based model on a set of evaluation tasks with a fixed training procedure. |
Improving Span Representation by Efficient Span-Level Attention (2023.findings-emnlp)
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| Challenge: | Existing methods for generating high-quality span representations are limited by subset of tokens . span-span interactions should play an important role in span encoding, authors argue . |
| Approach: | They propose to introduce span-span interactions and more comprehensive span-token interactions to improve span representations. |
| Outcome: | The proposed model outperforms baseline models on span-related tasks and shows superior performance. |
Token-level and sequence-level loss smoothing for RNN language models (P18-1)
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| Challenge: | Maximum likelihood estimation treats all sentences that do not match the ground truth as equally poor, ignoring the structure of the output space. |
| Approach: | They propose to extend the reward augmented maximum likelihood approach to token-level loss smoothing by using token-based approaches to improve the model's performance. |
| Outcome: | The proposed model improves on image captioning and machine translation tasks and treats all sentences that do not match the ground truth as poor . |
Learning Sentence Representations over Tree Structures for Target-Dependent Classification (N18-1)
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| Challenge: | Existing work on tree structures uses syntactic parsers or Treebank annotations to perform target-dependent classifications. |
| Approach: | They propose a reinforcement learning based approach which automatically induces target-specific sentence representations over tree structures. |
| Outcome: | The proposed model gives superior performance on two benchmark tasks compared to previous work on parsed trees . |
Empowering Character-level Text Infilling by Eliminating Sub-Tokens (2024.acl-long)
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| Challenge: | Existing methods for character-level infilling relied on predicting sub-tokens, but this strategy was ineffective. |
| Approach: | They propose a method to fill-in-the-mid with Starting and Ending character constraints that avoids predicting sub-tokens in inference. |
| Outcome: | The proposed method surpasses existing methods and offers significant performance gains. |
Where are we Still Split on Tokenization? (2024.findings-eacl)
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| Challenge: | Identifying tokens is a crucial first step for many tasks in Natural Language Processing (NLP) gold tokenization is often assumed, but some work on token-level tasks is more challenging. |
| Approach: | They propose an efficient method for tokenization with subword-based language models and evaluate it on 122 languages in 20 scripts. |
| Outcome: | The proposed method performs on par with the state-of-the-art on 122 languages in 20 scripts. |
Exploring morphology-aware tokenization: A case study on Spanish language modeling (2025.emnlp-main)
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| Challenge: | a recent study shows that subword tokenization improves performance of neural language models. |
| Approach: | They propose a linguistically grounded approach to train a tokenizer on morphologically segmented data. |
| Outcome: | The proposed tokenizer improves on a Spanish language model with morphological information. |
Multi-Agent Mutual Learning at Sentence-Level and Token-Level for Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | Neural machine translation (NMT) has achieved significant progress over recent years. |
| Approach: | They extend mutual learning to the machine translation task and operate at both the sentence-level and the token-level. |
| Outcome: | The proposed method improves on the IWSLT’14 German-English task and also on the WMT’14 English-German task. |