Elevating Code-mixed Text Handling through Auditory Information of Words (2023.emnlp-main)
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| Challenge: | Current language models focus on the semantic representation of words and ignore the auditory phonetic features. |
| Approach: | They propose an approach to create language models for handling code-mixed textual data using auditory phonetic features from SOUNDEX using auditorian information. |
| Outcome: | The proposed approach improves robustness against adversarial attacks on code-mixed classification tasks and improves classification results over baselines. |
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| Challenge: | mllm-shap is an open-source Python platform for researchers and ML practitioners that extends Shapley value (SV) explainability from text-only large languagemodels to multimodal LLMs that process both text and audio. |
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CLAPSpeech: Learning Prosody from Text Context with Contrastive Language-Audio Pre-Training (2023.acl-long)
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| Challenge: | Existing methods for expressive text-to-speech only implicitly learn prosody with masked token reconstruction tasks. |
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Processing and Understanding Mixed Language Data (D19-2)
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| Challenge: | Multilingual communities exhibit code-mixing, mixing of two or more languages in a single conversation . social media and other informal interactive platforms are allowing code-switching in user-generated text . |
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| Challenge: | a recent study has focused on audio deepfake detection (ADD) due to its ability to impersonate and share false, often malicious information. |
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| Challenge: | Existing methods to synthesize speech for low-resource languages require a substantial amount of source language corpora to generate the linguistic knowledge that can be reused for speech synthesis. |
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| Challenge: | a new approach to natural language processing uses arbitrary symbols to represent meaning . Soundex, MetaPhone, NYSIIS, logogram are used as inputs for NLP . |
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| Challenge: | Code-mixed (CM) language training is a difficult problem because of lack of data and the increased confusability due to the presence of more than one language. |
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Pavan Sai Balaga, Nagasamudram Karthik, Challa Vishwanath, Raksha Sharma, Rudra Murthy, Ashish Mittal
| Challenge: | Code-mixed text presents significant challenges for machine learning due to interplay of distinct grammatical structures, effectively forming a hybrid language. |
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CDLM: Cross-Document Language Modeling (2021.findings-emnlp)
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| Challenge: | Existing language models (LMs) provide powerful representations for internal text structure, but there are important applications for multi-text tasks. |
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