| 1. |
- Adelani, David Ifeoluwa, et al.
(author)
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MasakhaNER: Named Entity Recognition for African Languages
- 2021
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In: Transactions of the Association for Computational Linguistics. - : MIT Press. - 2307-387X. ; 9, s. 1116-1131
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Journal article (peer-reviewed)abstract
- We take a step towards addressing the under-representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state-of-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP.
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| 2. |
- Beck, Daniel, et al.
(author)
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Learning Structural Kernels for Natural Language Processing
- 2015
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In: Transactions of the Association for Computational Linguistics. - Stroudsburg, PA : Association for Computational Linguistics. - 2307-387X. ; 3, s. 461-473
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Journal article (peer-reviewed)abstract
- Structural kernels are a flexible learning paradigm that has been widely used in Natural Language Processing. However, the problem of model selection in kernel-based methods is usually overlooked. Previous approaches mostly rely on setting default values for kernel hyperparameters or using grid search, which is slow and coarse-grained. In contrast, Bayesian methods allow efficient model selection by maximizing the evidence on the training data through gradient-based methods. In this paper we show how to perform this in the context of structural kernels by using Gaussian Processes. Experimental results on tree kernels show that this procedure results in better prediction performance compared to hyperparameter optimization via grid search. The framework proposed in this paper can be adapted to other structures besides trees, e.g., strings and graphs, thereby extending the utility of kernel-based methods.
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| 3. |
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| 4. |
- Hong, X. D., et al.
(author)
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Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences
- 2023
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In: Transactions of the Association for Computational Linguistics. - 2307-387X. ; 11, s. 565-581
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Journal article (peer-reviewed)abstract
- Current work on image-based story generation suffers from the fact that the existing image sequence collections do not have coherent plots behind them. We improve visual story generation by producing a new image-grounded dataset, Visual Writing Prompts (VWP). VWP contains almost 2K selected sequences of movie shots, each including 5-10 images. The image sequences are aligned with a total of 12K stories which were collected via crowdsourcing given the image sequences and a set of grounded characters from the corresponding image sequence. Our new image sequence collection and filtering process has allowed us to obtain stories that are more coherent, diverse, and visually grounded compared to previous work. We also propose a character-based story generation model driven by coherence as a strong baseline. Evaluations show that our generated stories are more coherent, visually grounded, and diverse than stories generated with the current state-of-the-art model. Our code, image features, annotations and collected stories are available at .
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| 5. |
- Kuhlmann, Marco, 1977-, et al.
(author)
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A New Parsing Algorithm for Combinatory Categorial Grammar
- 2014
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In: Transactions of the Association for Computational Linguistics. - : Association for Computational Linguistics. - 2307-387X. ; 2:2014, s. 405-418
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Journal article (peer-reviewed)abstract
- We present a polynomial-time parsing algorithm for CCG, based on a new decomposition of derivations into small, shareable parts. Our algorithm has the same asymptotic complexity, O(n⁶), as a previous algorithm by Vijay-Shanker and Weir (1993), but is easier to understand, implement, and prove correct.
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| 6. |
- Kuhlmann, Marco, 1977-, et al.
(author)
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Parsing to Noncrossing Dependency Graphs
- 2015
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In: Transactions of the Association for Computational Linguistics. - : Association for Computational Linguistics. - 2307-387X. ; 3, s. 559-570
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Journal article (peer-reviewed)abstract
- We study the generalization of maximum spanning tree dependency parsing to maximum acyclic subgraphs. Because the underlying optimization problem is intractable even under an arc-factored model, we consider the restriction to noncrossing dependency graphs. Our main contribution is a cubic-time exact inference algorithm for this class. We extend this algorithm into a practical parser and evaluate its performance on four linguistic data sets used in semantic dependency parsing. We also explore a generalization of our parsing framework to dependency graphs with pagenumber at most $k$ and show that the resulting optimization problem is NP-hard for k ≥ 2.
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| 7. |
- Lau, Jey Han, et al.
(author)
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How Furiously Can Colorless Green Ideas Sleep? Sentence Acceptability in Context
- 2020
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In: Transactions of the Association of Computational Linguistics. - : MIT Press. - 2307-387X. ; 8, s. 296-310
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Journal article (peer-reviewed)abstract
- We study the influence of context on sentence acceptability. First we compare the acceptability ratings of sentences judged in isolation, with a relevant context, and with an irrelevant context. Our results show that context induces a cognitive load for humans, which compresses the distribution of ratings. Moreover, in relevant contexts we observe a discourse coherence effect that uniformly raises acceptability. Next, we test unidirectional and bidirectional language models in their ability to predict acceptability ratings. The bidirectional models show very promising results, with the best model achieving a new state-of-the-art for unsupervised acceptability prediction. The two sets of experiments provide insights into the cognitive aspects of sentence processing and central issues in the computational modeling of text and discourse.
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| 8. |
- Satta, Giorgio, et al.
(author)
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Efficient Parsing for Head-Split Dependency Trees
- 2013
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In: Transactions of the Association for Computational Linguistics. - Stroudsburg, PA, USA : Association for Computational Linguistics. - 2307-387X. ; 1:July, s. 267-278
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Journal article (other academic/artistic)abstract
- Head splitting techniques have been successfully exploited to improve the asymptotic runtime of parsing algorithms for projective dependency trees, under the arc-factored model. In this article we extend these techniques to a class of non-projective dependency trees, called well-nested dependency trees with block-degree at most 2, which has been previously investigated in the literature. We define a structural property that allows head splitting for these trees, and present two algorithms that improve over the runtime of existing algorithms at no significant loss in coverage.
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| 9. |
- Shao, Yan, 1990-, et al.
(author)
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Universal Word Segmentation : Implementation and Interpretation
- 2018
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In: Transactions of the Association for Computational Linguistics. - 2307-387X. ; 6, s. 421-435
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Journal article (peer-reviewed)abstract
- Word segmentation is a low-level NLP taskt hat is non-trivial for a considerable number of languages. In this paper, we present asequence tagging framework and apply it to word segmentation for a wide range of languages with different writing systems and typological characteristics. Additionally, we investigate the correlations between various typological factors and word segmentation accuracy. The experimental results indicate that segmentation accuracy is positively related to word boundary markers and negatively to the number of unique non-segmental terms. Based on the analysis, we design a small set of language-specific settings and extensively evaluate the segmentation system on the Universal Dependencies datasets. Our model obtains state-of-the-art accuracies on all the UD languages. It performs substantially better on languages that are non-trivial to segment, such as Chinese, Japanese, Arabic and Hebrew, when compared to previous work.
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| 10. |
- Strobl, Lena, et al.
(author)
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What formal languages can transformers express? a survey
- 2024
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In: Transactions of the Association for Computational Linguistics. - : MIT Press. - 2307-387X. ; 12, s. 543-561
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Journal article (peer-reviewed)abstract
- As transformers have gained prominence in natural language processing, some researchers have investigated theoretically what problems they can and cannot solve, by treating problems as formal languages. Exploring such questions can help clarify the power of transformers relative to other models of computation, their fundamental capabilities and limits, and the impact of architectural choices. Work in this subarea has made considerable progress in recent years. Here, we undertake a comprehensive survey of this work, documenting the diverse assumptions that underlie different results and providing a unified framework for harmonizing seemingly contradictory findings.
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