Papers by Pascal Vincent
Flow Matching for Conditional Text Generation in a Few Sampling Steps (2024.eacl-short)
Copied to clipboard
| Challenge: | Current diffusion models face multiple drawbacks including slow sampling, noise schedule sensitivity, and misalignment between training and sampling stages. |
| Approach: | They propose a method which leverages flow matching for conditional text generation. |
| Outcome: | The proposed method can generate text in a few steps by training with a novel anchor loss, alleviating the need for expensive hyperparameter optimization of the noise schedule prevalent in diffusion models. |
Chop and Change: Anaphora Resolution in Instructional Cooking Videos (2022.findings-aacl)
Copied to clipboard
| Challenge: | temporally evolving entities present challenges for anaphora resolution tasks . recipes provide rich source for referring expressions of transformed entities . |
| Approach: | They propose to use annotations to annotate recipes for anaphora resolution task . they propose to employ temporal features to improve anamorphic resolution . |
| Outcome: | The proposed annotation scheme improves the performance of the anaphora resolution task. |
MMAR: Multilingual and Multimodal Anaphora Resolution in Instructional Videos (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to multilingual anaphora resolution include images and video inputs. |
| Approach: | They propose to include multimodal information in the form of images in anaphora resolution tasks. |
| Outcome: | The proposed approach improves resolution by 10% for unseen languages. |
Find-2-Find: Multitask Learning for Anaphora Resolution and Object Localization (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing systems require large number of accurate annotations, such as image-level labels and location-level labeling. |
| Approach: | They propose a joint anaphora resolution and object localization dataset targeting visual-linguistic ambiguity. |
| Outcome: | The proposed framework improves visual-linguistic alignment and object localization with one joint model compared to a strong single-task baseline. |
Auto-Encoding Dictionary Definitions into Consistent Word Embeddings (D18-1)
Copied to clipboard
| Challenge: | Monolingual dictionaries are widespread and semantically rich resources. |
| Approach: | They propose a model that learns to compute word embeddings by processing dictionary definitions and trying to reconstruct them. |
| Outcome: | The proposed model shows strong performance when trained exclusively on dictionary data and generalizes in one shot. |
The Emergence of Argument Structure in Artificial Languages (2022.tacl-1)
Copied to clipboard
| Challenge: | a new study shows that natural languages are shaped by cognitive and sociocultural factors. |
| Approach: | They propose a setup where agents talk about a variable number of entities that can be partially observed by the listener. |
| Outcome: | The proposed setup shows that awareness of object structure yields a more natural sentence organization. |
Do sequence-to-sequence VAEs learn global features of sentences? (2020.emnlp-main)
Copied to clipboard
| Challenge: | Autoregressive language models are often trained without explicit conditioning labels . authors question claim that latent vectors can capture global features in unsupervised manner . |
| Approach: | They propose to use a sequence-to-sequence architecture to learn latent variables . they find that VAEs are prone to memorizing the first words and sentence length . |
| Outcome: | The proposed model is prone to memorizing the first words and sentence length, the authors show . et al., 2016: a new model learns latent variables that are more global, more predictive of topic or topic labels . authors question this claim, say it is a waste of time and money . |