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An Overview of Indian Spoken Language Recognition from Machine Learning Perspective
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In: ISSN: 2375-4699 ; EISSN: 2375-4702 ; ACM Transactions on Asian and Low-Resource Language Information Processing ; https://hal.inria.fr/hal-03616853 ; ACM Transactions on Asian and Low-Resource Language Information Processing, ACM, In press, ⟨10.1145/3523179⟩ (2022)
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Assessing the impact of OCR noise on multilingual event detection over digitised documents
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In: ISSN: 1432-5012 ; EISSN: 1432-1300 ; International Journal on Digital Libraries ; https://hal.archives-ouvertes.fr/hal-03635985 ; International Journal on Digital Libraries, Springer Verlag, 2022, ⟨10.1007/s00799-022-00325-2⟩ (2022)
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Introducing the HIPE 2022 Shared Task: Named Entity Recognition and Linking in Multilingual Historical Documents
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In: Advances in Information Retrieval. 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part II ; https://hal.archives-ouvertes.fr/hal-03635971 ; Matthias Hagen; Suzan Verberne; Craig Macdonald; Christin Seifert; Krisztian Balog; Kjetil Nørvåg; Vinay Setty. Advances in Information Retrieval. 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part II, 13186, Springer International Publishing, pp.347-354, 2022, Lecture Notes in Computer Science, 978-3-030-99738-0. ⟨10.1007/978-3-030-99739-7_44⟩ (2022)
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Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios?
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In: Seventh Workshop on Noisy User-generated Text (W-NUT 2021, colocated with EMNLP 2021) ; https://hal.inria.fr/hal-03527328 ; Seventh Workshop on Noisy User-generated Text (W-NUT 2021, colocated with EMNLP 2021), Jan 2022, punta cana, Dominican Republic ; https://aclanthology.org/2021.wnut-1.47/ (2022)
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Cross-lingual few-shot hate speech and offensive language detection using meta learning
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In: ISSN: 2169-3536 ; EISSN: 2169-3536 ; IEEE Access ; https://hal.archives-ouvertes.fr/hal-03559484 ; IEEE Access, IEEE, 2022, 10, pp.14880-14896. ⟨10.1109/ACCESS.2022.3147588⟩ (2022)
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Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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A Neural Pairwise Ranking Model for Readability Assessment ...
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A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognition ...
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pNLP-Mixer: an Efficient all-MLP Architecture for Language ...
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MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages ...
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Adapting BigScience Multilingual Model to Unseen Languages ...
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Does Corpus Quality Really Matter for Low-Resource Languages? ...
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A New Generation of Perspective API: Efficient Multilingual Character-level Transformers ...
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Learning Bidirectional Translation between Descriptions and Actions with Small Paired Data ...
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Abstract:
This study achieved bidirectional translation between descriptions and actions using small paired data. The ability to mutually generate descriptions and actions is essential for robots to collaborate with humans in their daily lives. The robot is required to associate real-world objects with linguistic expressions, and large-scale paired data are required for machine learning approaches. However, a paired dataset is expensive to construct and difficult to collect. This study proposes a two-stage training method for bidirectional translation. In the proposed method, we train recurrent autoencoders (RAEs) for descriptions and actions with a large amount of non-paired data. Then, we fine-tune the entire model to bind their intermediate representations using small paired data. Because the data used for pre-training do not require pairing, behavior-only data or a large language corpus can be used. We experimentally evaluated our method using a paired dataset consisting of motion-captured actions and ... : 8 pages, 7 figures. Submitted to RA-L (IEEE Robotics and Automation Letters) with IROS 2022 Option. An accompanying video is available at https://youtu.be/YlxM_kw6YLE ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG; Robotics cs.RO
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URL: https://dx.doi.org/10.48550/arxiv.2203.04218 https://arxiv.org/abs/2203.04218
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