Papers with processing tasks
Getting BART to Ride the Idiomatic Train: Learning to Represent Idiomatic Expressions (2022.tacl-1)
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| Challenge: | Prior work has identified deficiencies in their contextualized representation stemming from the underlying compositional paradigm of representation. |
| Approach: | They propose to use an adapter as a lightweight non-compositional language expert trained on idiomatic sentences to build idiomity into BART. |
| Outcome: | The proposed approach improves idiomaticity over baselines and up to 25% higher sequence accuracy on idiom processing tasks. |
Multilingual Image Corpus – Towards a Multimodal and Multilingual Dataset (2022.lrec-1)
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| Challenge: | The goal of the project Multilingual Image Corpus is to provide a large image dataset with annotated objects and object descriptions in 24 languages. |
| Approach: | They propose to provide a large image dataset with annotated objects and object descriptions in 24 languages. |
| Outcome: | The project provides a large image dataset with annotated objects and object descriptions in 24 languages. |
Modular and Parameter-Efficient Multimodal Fusion with Prompting (2022.findings-acl)
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| Challenge: | Recent research has made impressive progress in large-scale multimodal pre-training. |
| Approach: | They propose to use prompt vectors to align multimodal modalities by pretraining text inputs with prompts or embedding vectors. |
| Outcome: | The proposed method achieves comparable performance to several other multimodal fusion methods in low-resource settings. |
Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit (D18-1)
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Amrith Krishna, Bishal Santra, Sasi Prasanth Bandaru, Gaurav Sahu, Vishnu Dutt Sharma, Pavankumar Satuluri, Pawan Goyal
| Challenge: | a structured prediction framework is proposed to solve word segmentation and morphological tagging tasks in a free word order language. |
| Approach: | They propose a structured prediction framework that jointly solves word segmentation and morphological tagging tasks in Sanskrit. |
| Outcome: | The proposed model outperforms the state of the art with an F-Score of 96.92 (percentage improvement of 7.06%) while using less than one tenth of the task-specific training data. |
Finely Tuned, 2 Billion Token Based Word Embeddings for Portuguese (L18-1)
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| Challenge: | A distributional semantics model is instrumental to improve the performance of many applications and processing tasks for any language. |
| Approach: | They propose to develop an advanced distributional model for Portuguese with the largest vocabulary and best evaluation scores published so far. |
| Outcome: | The proposed model has the largest vocabulary and the best evaluation scores published so far. |
Modeling Collaborative Multimodal Behavior in Group Dialogues: The MULTISIMO Corpus (L18-1)
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| Challenge: | a corpus of human-computer interactions recorded in multiple modalities is being developed to study and model collaborative aspects of multimodal behavior in groups. |
| Approach: | They propose to use a multimodal corpus to investigate collaborative aspects of multimodal behavior in groups that perform simple tasks. |
| Outcome: | The proposed corpus is designed for public release and includes survey materials, personality tests and experience assessment questionnaires filled in by all participants. |