| 1. |
- Boholm, Max, 1982, et al.
(författare)
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Political dogwhistles and community divergence in semantic change
- 2023
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Ingår i: Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change. - : Association for Computational Linguistics.
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Konferensbidrag (refereegranskat)abstract
- We test whether the development of political dogwhistles can be observed using language change measures; specifically, does the development of a “hidden” message in a dogwhistle show up as differences in semantic change between communities over time? We take Swedish-language dogwhistles related to the on-going immigration debate and measure differences over time in their rate of semantic change between two Swedish-language community forums, Flashback and Familjeliv, the former representing an in-group for understanding the “hidden” meaning of the dogwhistles. We find that multiple measures are sensitive enough to detect differences over time, in that the meaning changes in Flashback over the relevant time period but not in Familjeliv. We also examine the sensitivity of multiple modeling approaches to semantic change in the matter of community divergence.
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| 2. |
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| 3. |
- Hong, Xudong, et al.
(författare)
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A surprisal oracle for active curriculum language modeling
- 2023
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Ingår i: Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning, December 6-7, 2023, Singapore / Alex Warstadt, Aaron Mueller, Leshem Choshen, Ethan Wilcox, Chengxu Zhuang, Juan Ciro, Rafael Mosquera, Bhargavi Paranjabe, Adina Williams, Tal Linzen, Ryan Cotterell (Editors). - : Association for Computational Linguistics. - 9781952148026
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Konferensbidrag (refereegranskat)abstract
- We investigate the viability of surprisal in an active curriculum learning framework to train transformer-based language models in the context of the BabyLM Challenge. In our approach, the model itself selects the data to label (active learning) and schedules data samples based on a surprisal oracle (curriculum learning). We show that the models learn across all the tasks and datasets evaluated, making the technique a promising alternative approach to reducing the data requirements of language models. Our code is available at https://github.com/asayeed/ActiveBaby
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| 4. |
- Hong, X. D., et al.
(författare)
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Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences
- 2023
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Ingår i: Transactions of the Association for Computational Linguistics. - 2307-387X. ; 11, s. 565-581
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Tidskriftsartikel (refereegranskat)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. |
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| 6. |
- Hong, X., et al.
(författare)
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Learning distributed event representations with a multi-task approach
- 2018
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Ingår i: Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, New Orleans, June 5-6, 2018.. - Stroudsburg, PA, USA : Association for Computational Linguistics. - 9781948087223
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Konferensbidrag (refereegranskat)abstract
- Human world knowledge contains information about prototypical events and their participants and locations. In this paper, we train the first models using multi-task learning that can both predict missing event participants and also perform semantic role classification based on semantic plausibility. Our best-performing model is an improvement over the previous state-of-the-art on thematic fit modelling tasks. The event embeddings learned by the model can additionally be used effectively in an event similarity task, also outperforming the state-of-the-art.
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| 7. |
- Hong, Xudong, et al.
(författare)
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Visual Coherence Loss for Coherent and Visually Grounded Story Generation
- 2023
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Ingår i: Proceedings of the Annual Meeting of the Association for Computational Linguistics. - 0736-587X. - 9781959429777
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Konferensbidrag (refereegranskat)abstract
- Local coherence is essential for text generation models. We identify two important aspects of local coherence within the visual storytelling task: (1) the model needs to represent re-occurrences of characters within the image sequence in order to mention them correctly in the story; (2) character representations should enable us to find instances of the same characters and distinguish different characters. In this paper, we propose a loss function inspired by a linguistic theory of coherence for learning image sequence representations. We further propose combining features from an object detector and a face detector to construct stronger character features. To evaluate visual grounding that current reference-based metrics do not measure, we propose a character matching metric to check whether the models generate referring expressions correctly for characters in input image sequences. Experiments on a visual story generation dataset show that our proposed features and loss function are effective for generating more coherent and visually grounded stories. Our code is available at https://github.com/vwprompt/vcl.
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| 8. |
- Kågebäck, Mikael, 1981, et al.
(författare)
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A reinforcement-learning approach to efficient communication
- 2020
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 15:7
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Tidskriftsartikel (refereegranskat)abstract
- We present a multi-agent computational approach to partitioning semantic spaces using reinforcement-learning (RL). Two agents communicate using a finite linguistic vocabulary in order to convey a concept. This is tested in the color domain, and a natural reinforcement learning mechanism is shown to converge to a scheme that achieves a near-optimal trade-off of simplicity versus communication efficiency. Results are presented both on the communication efficiency as well as on analyses of the resulting partitions of the color space. The effect of varying environmental factors such as noise is also studied. These results suggest that RL offers a powerful and flexible computational framework that can contribute to the development of communication schemes for color names that are near-optimal in an information-theoretic sense and may shape color-naming systems across languages. Our approach is not specific to color and can be used to explore cross-language variation in other semantic domains.
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| 9. |
- Kågebäck, Mikael, 1981, et al.
(författare)
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DeepColor: Reinforcement Learning optimizes information efficiency and well-formedness in color name partitioning
- 2018
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Ingår i: CogSci 2018, 40th annual Cognitive Science Society meeting, Madison Wisconsin USA, July 25-28 2018. - Oakbrook Terrace, IL, USA : Cognitive Science Society. - 9780991196784
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Konferensbidrag (refereegranskat)abstract
- As observed in the World Color Survey (WCS), some universal properties can be identified in color naming schemes over a large number of languages. For example, Regier, Kay, and Khetrapal (2007) and Regier, Kemp, and Kay (2015); Gibson et al. (2017) recently explained these universal patterns in terms of near optimal color partitions and information theoretic measures of efficiency of communication. Here, we introduce a computational learning framework with multi-agent systems trained by reinforcement learning to investigate these universal properties. We compare the results with Regier et al. (2007, 2015) and show that our model achieves excellent quantitative agreement. This work introduces a multi-agent reinforcement learning framework as a powerful and versatile tool to investigate such semantic universals in many domains and contribute significantly to central questions in cognitive science.
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| 10. |
- Loáiciga, Sharid, 1986, et al.
(författare)
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Exploiting Cross-Lingual Hints to Discover Event Pronouns
- 2020
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Ingår i: Proceedings of the 12th Language Resources and Evaluation Conference, Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, and Stelios Piperidis (eds.), Marseille, France, 11–16 May 2020. - : The European Language Resources Association. - 9791095546344
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Konferensbidrag (refereegranskat)
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