Papers by Dmitry Nikolaev
Fine-Grained Analysis of Cross-Linguistic Syntactic Divergences (2020.acl-main)
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Dmitry Nikolaev, Ofir Arviv, Taelin Karidi, Neta Kenneth, Veronika Mitnik, Lilja Maria Saeboe, Omri Abend
| Challenge: | Existing work on quantifying the prevalence of syntactic divergences across languages has not been done. |
| Approach: | They propose a framework for extracting divergence patterns for any language pair from a parallel corpus building on Universal Dependencies. |
| Outcome: | The proposed framework provides a detailed picture of cross-language divergences, generalizes previous approaches, and lends itself to full automation. |
Representation biases in sentence transformers (2023.eacl-main)
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| Challenge: | argued that transformer-based models are not well suited for sentence-level downstream tasks. |
| Approach: | They propose to use sentence transformers to produce full-sentence representations . they propose to combine transformers with a training regime that embeds tokens into the model . |
| Outcome: | The proposed model performs better on downstream tasks than the vanilla model and its variants. |
Improving Cross-lingual Transfer through Subtree-aware Word Reordering (2023.findings-emnlp)
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| Challenge: | Recent studies show that multilingual language models are not effective when dealing with less-represented languages. |
| Approach: | They propose a powerful reordering method that learns word-order patterns conditioned on the syntactic context from a small amount of annotated data. |
| Outcome: | The proposed method outperforms baselines on a variety of tasks and is effective in both zero-shot and few-shot scenarios. |
An Attribution Method for Siamese Encoders (2023.emnlp-main)
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| Challenge: | Despite the success of Siamese encoder models, little is known about the aspects of inputs they base their decisions on. |
| Approach: | They propose a local attribution method for Siamese encoders by generalizing the principle of integrated gradients to models with multiple inputs. |
| Outcome: | The proposed method can be reduced to a token–token matrix and account for the model’s full computation graph and is guaranteed to converge to the actual prediction. |
Approximate Attributions for Off-the-Shelf Siamese Transformers (2024.eacl-long)
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| Challenge: | Existing attribution methods cannot tackle Siamese encoders since they compare two inputs rather than processing a single one. |
| Approach: | They propose an attribution method specifically targeted for Siamese encoders that can be adjusted and fine-tuned to retain original model's predictive performance. |
| Outcome: | The proposed method retains the original model's predictive performance and can be applied to off-the-shelf models. |
Adverbs, Surprisingly (2023.starsem-1)
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| Challenge: | adverbs are the part of speech (POS) that has seen the least attention in computational linguistics due to its challenging nature. |
| Approach: | They propose to use Frame Semantics to characterize word meaning to uncover systematic gaps in adverb accounts. |
| Outcome: | The proposed approach can describe ambiguity, semantic roles, and null instantiation of adverbs. |
Multilingual estimation of political-party positioning: From label aggregation to long-input Transformers (2023.emnlp-main)
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| Challenge: | Scaling analysis is a technique that assigns a political actor a score on a predefined scale based on 'typically long' text. |
| Approach: | They propose to use label aggregation and long-input-Transformer-based models to automatically scale political-party manifestos. |
| Outcome: | The proposed models can scale political platforms on a predefined scale based on 'left-right' scales and work robustly across domains and languages. |
On the Relation between Syntactic Divergence and Zero-Shot Performance (2021.emnlp-main)
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| Challenge: | Recent advances in cross-lingual transfer methods have enabled significant advances in grammatical processing tasks. |
| Approach: | They examine the extent to which syntactic relations are preserved in translation and parsability in a zero-shot setting. |
| Outcome: | The proposed model is based on a translation task in English and a subset of a standard English RE benchmark translated to Russian and Korean. |
Additive manifesto decomposition: A policy domain aware method for understanding party positioning (2023.findings-acl)
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| Challenge: | Existing methods for estimating policy domain aware party similarity are limited to global domains. |
| Approach: | They propose a workflow for estimating policy domain aware party similarity by aggregating policy domains into a single figure . they use a set of tools to extract party positions on major policy axes via multidimensional scaling. |
| Outcome: | The proposed method yields high correlation when predicting party similarity at a global level and provides accurate party-specific positions even with automatically labelled policy domains. |
SegBo: A Database of Borrowed Sounds in the World’s Languages (2020.lrec-1)
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| Challenge: | phonological segment borrowing is a process through which languages acquire new contrastive speech sounds as the result of borrowing words from other languages. |
| Approach: | They propose to use a database to aggregate borrowed phonological segments from languages to create a new contrastive sound. |
| Outcome: | The proposed database is based on a cross-linguistic database of borrowed phonological segments. |