Papers by Joakim Nivre
Attention Can Reflect Syntactic Structure (If You Let It) (2021.eacl-main)
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| Challenge: | a recent study has attempted to decode linguistic structure from the Transformer . but, much of the work focused on English, a language with rigid word order and a lack of inflectional morphology. |
| Approach: | They propose to fine-tune a feature encoder for BERT to learn linguistic structure from its multi-head attention mechanism. |
| Outcome: | The proposed model can decode full trees above baseline accuracy from single attention heads across languages. |
UCxn: Typologically-Informed Annotation of Constructions Atop Universal Dependencies (2024.lrec-main)
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Leonie Weissweiler, Nina Böbel, Kirian Guiller, Santiago Herrera, Wesley Samuel Scivetti, Arthur Lorenzi, Nurit Melnik, Archna Bhatia, Hinrich Schütze, Lori Levin, Amir Zeldes, Joakim Nivre, William Croft, Nathan Schneider
| Challenge: | Grammatical constructions that convey meaning through a particular combination of several morphosyntactic elements are not labeled holistically. |
| Approach: | They propose to augment UD annotations with a ‘UCxn’ annotation layer for such meaning-bearing grammatical constructions and to approach this in a typologically informed way so that morphosyntactic strategies can be compared across languages. |
| Outcome: | The proposed annotation layer could be used to annotate meaning-bearing constructions across languages and to compare them across languages. |
An Investigation of the Interactions Between Pre-Trained Word Embeddings, Character Models and POS Tags in Dependency Parsing (D18-1)
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| Challenge: | Existing studies have shown that character models are less important in the presence of word embeddings, but combining them quickly leads to diminishing returns. |
| Approach: | They propose to combine pre-trained word embeddings, character models and POS tags to improve parsing quality by categorising words by frequency, POS tag and language. |
| Outcome: | The proposed system improves on initialised word embeddings but combines them quickly leads to diminishing returns. |
Syntactic Nuclei in Dependency Parsing – A Multilingual Exploration (2021.eacl-main)
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| Challenge: | Existing models for syntactic dependency parsing assume words are elementary units that enter into dependency relations. |
| Approach: | They propose to use composition functions to make a transition-based dependency parser aware of the notion of nucleus. |
| Outcome: | The proposed concept of nucleus gives small but significant improvements in parsing accuracy on 12 languages. |
Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection (2020.lrec-1)
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Joakim Nivre, Marie-Catherine de Marneffe, Filip Ginter, Jan Hajič, Christopher D. Manning, Sampo Pyysalo, Sebastian Schuster, Francis Tyers, Daniel Zeman
| Challenge: | Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages. |
| Approach: | They describe version 2 of the universal guidelines and discuss major changes from UD v1 to UD 2 . they propose a morphological layer, a syntactic layer and a word segmentation layer . |
| Outcome: | The proposed treebanks are available for 90 languages and have been updated to meet the needs of multilingual parsers and researchers. |
A Tale of Three Parsers: Towards Diagnostic Evaluation for Meaning Representation Parsing (2020.lrec-1)
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| Challenge: | Empirical results suggest that the proposed methodology can be meaningfully applied to parsing into graph-structured target representations, uncovering hitherto unknown properties of the different approaches. |
| Approach: | They propose to map from natural language utterances to graph-based encodings of its semantic structure using contrastive and diagnostic evaluation techniques. |
| Outcome: | The proposed method can be meaningfully applied to parsing into graph-structured target representations, uncovering hitherto unknown properties of the different systems that can inform future development and cross-fertilization across approaches. |
Fine-Grained Controllable Text Generation Using Non-Residual Prompting (2022.acl-long)
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| Challenge: | Existing approaches to control the text generation process are not expressive enough. |
| Approach: | They propose an encoder-decoder architecture that enables intermediate text prompts at arbitrary time steps. |
| Outcome: | The proposed architecture is expressive and versatile on multiple experimental settings. |
The Effects of Corpus Choice and Morphosyntax on Multilingual Space Induction (2022.findings-emnlp)
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| Challenge: | Prior work on inductive biases of language models towards natural language has focused on quantifying their ability to build multilingual spaces. |
| Approach: | They propose to use linguistically motivated tasks as a proxy to study inductive biases of language models with respect to natural language phenomena to build multilingual embedding spaces. |
| Outcome: | The proposed model performance is compared with other models using a set of linguistically motivated tasks and a training corpus in 15 languages. |
Recursive Subtree Composition in LSTM-Based Dependency Parsing (N19-1)
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| Challenge: | Existing studies show that tree structure modelling on top of sequence modelling is not feasible. |
| Approach: | They propose to recursively compose subtree representations in a biLSTM-based parser to capture subtreas. |
| Outcome: | The proposed model improves performance under ablating the backward LSTM and the forward LS. |
Encoders Help You Disambiguate Word Senses in Neural Machine Translation (D19-1)
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| Challenge: | Neural machine translation models can perform word sense disambiguation (WSD) however, it is unclear which component dominates the process of disambiguating words. |
| Approach: | They evaluate hidden states and investigate distributions of self-attention in NMT encoders and decoders to disambiguate word senses. |
| Outcome: | The proposed model outperforms encoder hidden states on large datasets . the model outpersforms decoders on large data sets . |
Sentences with Gapping: Parsing and Reconstructing Elided Predicates (N18-1)
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| Challenge: | Sentences with gapping lack an overt predicate to indicate the relation between two or more arguments. |
| Approach: | They propose two methods for parsing to a Universal Dependencies graph representation that explicitly encodes the elided material with additional nodes and edges. |
| Outcome: | The proposed methods reconstruct elided material from dependency trees with high accuracy when the parser correctly predicts the existence of a gap. |
MultiBLiMP 1.0: A Massively Multilingual Benchmark of Linguistic Minimal Pairs (2026.tacl-1)
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| Challenge: | MultiBLiMP 1.0 is a massively multilingual benchmark of linguistic minimal pairs covering 101 languages and 2 types of subject-verb agreement. |
| Approach: | They propose to use multilingual benchmarks to evaluate linguistic minimal pairs in 101 languages and 2 types of subject-verb agreement to create the minimal pairs. |
| Outcome: | The proposed benchmark covers 101 languages and 2 types of subject-verb agreement, and contains more than 128,000 minimal pairs. |
Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English (2020.coling-main)
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| Challenge: | Recent work shows that deeper character-based neural machine translation models outperform subword-based models. |
| Approach: | They propose to investigate the ability of character-based models to learn word senses and morphological inflections and the attention mechanism in Finnish into English translation. |
| Outcome: | The character-based models outperform subword-based model in Finnish to English translation. |
Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing - A Tale of Two Parsers Revisited (D19-1)
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| Challenge: | In recent years, dependency parsing has shifted from discrete features to neural networks and continuous representations. |
| Approach: | They propose to use deep contextualized word embeddings to pack information about global sentence structure into local feature representations to make the two approaches virtually equivalent in terms of accuracy and error profile. |
| Outcome: | The proposed model improves the accuracy and error profile of transition-based and graph-based dependency parsers on 13 languages. |
Parser Training with Heterogeneous Treebanks (P18-2)
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| Challenge: | In the 2017 CoNLL Shared Task on Universal Dependency Parsing, 25 languages have more than one treebank . many teams did not take advantage of the multiple treebanks, however, and trained one model per treebank instead of one model for each language. |
| Approach: | They propose a method to make the most of heterogeneous treebanks when training a monolingual parser. |
| Outcome: | The proposed method improves on training with multiple treebanks for a single language. |
Investigating UD Treebanks via Dataset Difficulty Measures (2023.eacl-main)
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| Challenge: | Treebanks annotated with Universal Dependencies (UD) are currently available for over 100 languages and are only partially reflected in parser evaluations via accuracy metrics like LAS. |
| Approach: | They propose to use dataset cartography, V-information, and minimum description length to analyze UD treebanks using three accuracy-free methods to provide insights about them. |
| Outcome: | The proposed methods provide insights about UD treebanks that would remain undetected if only LAS was considered. |
Do Neural Language Models Show Preferences for Syntactic Formalisms? (2020.acl-main)
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| Challenge: | Recent work on interpretability of deep neural language models concludes that many properties of natural language syntax are encoded in their representational spaces. |
| Approach: | They propose to examine whether syntactic structure adheres to a surface-syntactical or deep syntaktic style of analysis. |
| Outcome: | The proposed model prefers Universal Dependencies (UD) over Surface-Syntactic Universal Dependency (SUD) with interesting variations across languages and layers. |
Revisiting Negation in Neural Machine Translation (2021.tacl-1)
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| Challenge: | Negation is an important linguistic phenomenon in machine translation, as errors in translating negation may change the meaning of source sentences completely. |
| Approach: | They evaluate the translation of negation in English–German (EN–DE) and English– Chinese (EN-ZH) . they find that NMT models can distinguish negation and non-negation tokens very well and encode a lot of information about negation . |
| Outcome: | The accuracy of manual evaluation in ENDE, DEEN, ENZH, and ZHEN is 95.7%, 94.8%, 93.4%, and 91.7% respectively. |
An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization (C18-1)
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| Challenge: | In this paper, we apply different NMT models to the problem of historical spelling normalization for five languages . we find that NMT model is much better than SMT in terms of character error rate . |
| Approach: | They propose to use NMT models to solve the problem of historical spelling normalization in five languages. |
| Outcome: | The proposed method improves historical spelling normalization for five languages. |