Papers with Human language
Strong and Simple Baselines for Multimodal Utterance Embeddings (N19-1)
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| Challenge: | Human language is a rich multimodal signal consisting of spoken words, facial expressions, body gestures, and vocal intonations. |
| Approach: | They propose two simple but strong baselines to learn embeddings of multimodal utterances by factorizing the utterant into unimodal factors. |
| Outcome: | The proposed models show that they can be derived in closed form while maintaining simplicity and efficiency during learning and inference. |
Putting Natural in Natural Language Processing (2023.findings-acl)
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| Challenge: | human language is firstly spoken and only secondarily written. |
| Approach: | aaron carroll: human language is firstly spoken and only secondarily written . carroll says the field of NLP has overwhelmingly focused on processing written language . he says the focus is on a subset of human language which is convenient to work with . |
| Outcome: | the ACL 2023 theme track urges the community to check the reality of the progress in NLP . |
Interpretable Emoji Prediction via Label-Wise Attention LSTMs (D18-1)
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| Challenge: | Emojis are the evolution of characterbased emoticons and are used to express ideas about a myriad of topics. |
| Approach: | They propose a label-wise attention mechanism to better understand emoji prediction . they propose to model e-mails with eojis and then label them based on their meaning . |
| Outcome: | The proposed model improves over baselines and does particularly well when predicting infrequent emojis. |
Multimodal Transformer for Unaligned Multimodal Language Sequences (P19-1)
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Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, J. Zico Kolter, Louis-Philippe Morency, Ruslan Salakhutdinov
| Challenge: | Human language is often multimodal, which comprehends a mixture of natural language, facial gestures, and acoustic behaviors. |
| Approach: | They propose a multimodal model that extends the standard Transformer network to learn representations directly from unaligned multimodal streams. |
| Outcome: | The proposed model outperforms state-of-the-art methods on aligned and non-aligned data. |
Resource-Rational Noisy-Channel Language Processing: Testing the Effect of Algorithmic Constraints on Inferences (2025.emnlp-main)
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| Challenge: | a fundamental question in psycholinguistics is how comprehenders form interpretations of utterances that they hear or see. |
| Approach: | They propose to use a language model as a prior and an error model to encode likelihoods to perform incremental and approximate probabilistic inferences over intended sentences and production errors. |
| Outcome: | The proposed model captures previously established patterns in human sentence processing, and trade-off between human-like noisy-channel inferences and computational resources falls out of the model. |