Papers with SotA models
PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search (2023.eacl-main)
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| Challenge: | Existing benchmarks for phrase-similarity compare phrases alone (without context) and phrases with context (with or without context). |
| Approach: | They propose to use a dataset of 28K noun phrases accompanied by their contextual Wikipedia pages to train machine phrase embeddings. |
| Outcome: | The proposed dataset improves ranking-models’ accuracy and pushes span selection models near human accuracy, which is 95% Exact Match (EM) on semantic search given a query phrase and a passage. |
VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild (2024.acl-long)
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| Challenge: | VoiceCraft is a token-infilling neural codec language model for speech editing and zero-shot text-to-speech evaluation. |
| Approach: | They introduce a token infilling neural codec language model that performs on speech editing and zero-shot text-to-speech tasks. |
| Outcome: | The proposed model outperforms previous models on speech editing and zero-shot text-to-speech tasks. |
AliGATr: Graph-based layout generation for form understanding (2024.findings-emnlp)
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| Challenge: | State of the art forms understanding models often rely on poorly calibrated output probabilities and low performance on relation extraction tasks. |
| Approach: | They propose a graph-based model that uses a generative objective to represent complex grid-like layouts that are often found in forms. |
| Outcome: | The proposed model performs better on the KIE and RE tasks and is more accurate than existing models. |
Towards a new research agenda for multimodal enterprise document understanding: What are we missing? (2024.findings-acl)
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| Challenge: | In this paper, we discuss the limitations of multimodal document understanding models in enterprise settings. |
| Approach: | They propose a research agenda that is aimed at driving the field towards higher impact in enterprise applications. |
| Outcome: | The proposed research agenda is aimed at driving the field towards higher impact in enterprise applications. |