| Challenge: | Existing methods for reconstructing ancient word forms use expectation-maximization . past work has used this method to predict simple phonological changes . |
| Approach: | They extend expectation-maximization to predict phonological changes between ancient word forms and their cognates in modern languages. |
| Outcome: | The proposed model reduces edit distance from the target word forms compared to previous methods. |
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| Challenge: | Historical linguists have identified regularities in the process of historic sound change. |
| Approach: | They propose a method to reconstruct proto-words based on cognates in daughter languages . they use a dataset of 8,000 comparative entries to analyze phonological changes . |
| Outcome: | The proposed method outperforms conventional methods in a proto-word reconstruction task. |
Ab Initio: Automatic Latin Proto-word Reconstruction (C18-1)
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| Challenge: | Existing methods for proto-word reconstruction are time-consuming and manual, but few studies have done it . a recent study used cognates to reconstruct ancient languages from their modern counterparts . |
| Approach: | They propose to use Latin proto-words to automate the process of proto-language reconstruction . they leverage information from all modern languages and use conditional random fields for sequence labeling . |
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Verba volant, scripta volant? Don’t worry! There are computational solutions for protoword reconstruction (2024.emnlp-main)
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Liviu Dinu, Ana Uban, Alina Cristea, Ioan-Bogdan Iordache, Teodor-George Marchitan, Simona Georgescu, Laurentiu Zoicas
| Challenge: | Existing methods for protoword reconstruction are limited to a few languages. |
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Improved Neural Protoform Reconstruction via Reflex Prediction (2024.lrec-main)
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| Challenge: | comparative method allows linguists to infer protoforms from their reflexes based on sound change . authors argue that this approach ignores one of the most important aspects of the comparative approach . |
| Approach: | They propose a comparative method that allows linguists to infer protoforms from their reflexes . they propose to use a system where candidate protoform from a reconstruction model are reranked by a reflex prediction model. |
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Transformed Protoform Reconstruction (2023.acl-short)
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| Challenge: | Historical linguists reconstruct proto-languages by identifying systematic sound changes that can be inferred from correspondences between attested daughter languages. |
| Approach: | They propose to update their Latin protoform reconstruction model with the Transformer . romance data of 8,000 cognates spanning 5 languages and Chinese dataset are outperformed . |
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Cognate Transformer for Automated Phonological Reconstruction and Cognate Reflex Prediction (2023.emnlp-main)
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| Challenge: | Phonological reconstruction is one of the central problems in historical linguistics where a proto-word of an ancestral language is determined from the observed cognate words of daughter languages. |
| Approach: | They propose to use a protein language model to train on multiple sequence alignments to train a model on phonological reconstruction. |
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Phrase-Based & Neural Unsupervised Machine Translation (D18-1)
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| Challenge: | Recent advances in machine translation have reported near human-level performance on several languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences. |
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How to represent a word and predict it, too: Improving tied architectures for language modelling (D18-1)
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| Challenge: | Recent state-of-the-art models use word embeddings as input and output mappings instead of tied models. |
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| 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. |
Improving Neural Language Models by Segmenting, Attending, and Predicting the Future (P19-1)
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| Challenge: | Common language models typically predict the next word given a past context. |
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Learning to Discover, Ground and Use Words with Segmental Neural Language Models (P19-1)
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| 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. |
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