Papers with alignment models
EVALIGN: Visual Evaluation of Translation Alignment Models (2023.eacl-demo)
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| Challenge: | EvAlign is a visual analytics framework for quantitative and qualitative evaluation of automatic translation alignment models. |
| Approach: | They propose to use EvAlign to analyze automatic translation alignment models and compare their performance with other baseline and state-of-the-art models. |
| Outcome: | The framework hosts nine gold standard datasets and the predictions of multiple alignment models. |
Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change (P19-1)
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| Challenge: | State-of-the-art lexical semantic change detection models suffer from noise stemming from vector space alignment. |
| Approach: | They propose a method to simulate lexical semantic change and control for possible biases by avoiding alignment. |
| Outcome: | The proposed method outperforms state-of-the-art models on a synthetic task and a manual testset. |
The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation (2022.naacl-main)
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| Challenge: | Neural Machine Translation models can be optimized to improve latency by constraining the set of output words . lexical shortlisting fails to select the right set of input words for semantically non-compositional phenomena such as idiomatic expressions. |
| Approach: | They propose a model of vocabulary selection that constrains the set of allowed output words . they propose to increase the size of the allowed set to restore translation quality . |
| Outcome: | The proposed model restores translation quality of an unconstrained system, as measured by human evaluations on WMT newstest2020 and idiomatic expressions, at an inference latency competitive with alignment-based selection using aggressive thresholds. |
Alirector: Alignment-Enhanced Chinese Grammatical Error Corrector (2024.findings-acl)
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| Challenge: | Existing methods to address overcorrection in Chinese grammatical error correction (CGEC) are difficult to adapt to decoder-only large language models (LLMs). |
| Approach: | They propose an alignment-enhanced corrector for the overcorrection problem that applies to both Seq2Seq models and decoder-only large language models (LLMs). |
| Outcome: | The proposed corrector alleviates the overcorrection problem in Chinese grammatical error correction (CGEC) using generative models and decoder-only large language models. |
Jointly Learning to Align and Translate with Transformer Models (D19-1)
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| Challenge: | Existing word alignment models are not accurate for word alignments. |
| Approach: | They propose a method to train a Transformer model to produce accurate translations and alignments. |
| Outcome: | The proposed model outperforms GIZA++ trained models on translation and alignment tasks while maintaining translation accuracy. |
Multilingual Knowledge Graph Completion with Language-Sensitive Multi-Graph Attention (2023.acl-long)
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| Challenge: | Existing approaches to multilingual knowledge graph completion have two drawbacks: alignment dependency and training inefficiency. |
| Approach: | They propose a multilingual knowledge graph completion framework with language-sensitive multi-graph attention to predict missing links on all given KGs. |
| Outcome: | The proposed model improves on the DBP-5L and E-PKG datasets. |
German SRL: Corpus Construction and Model Training (2024.lrec-main)
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| Challenge: | Existing semantic role annotation resources are lacking for German. |
| Approach: | They propose a translation-based approach to train German semantic role models using semantic annotations and alignment models. |
| Outcome: | The proposed method achieves competitive evaluation scores, but avoids limitations of previous approaches. |