Papers by Antoine Bordes
Augmenting Transformers with KNN-Based Composite Memory for Dialog (2021.tacl-1)
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| Challenge: | Recent work has focused on learning architectures with large memories capable of storing external knowledge. |
| Approach: | They propose a method to augment generative Transformer neural networks with information fetching modules. |
| Outcome: | The proposed approach improves performance in generative dialog modeling . external knowledge is retrieved from Wikipedia, images, and human-written dialog utterances . |
Image-Chat: Engaging Grounded Conversations (2020.acl-main)
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| Challenge: | In order for machines to communicate with humans, they must understand the natural things that humans say about the world they live in and respond in kind. |
| Approach: | They propose to fuse a set of neural architectures using image and text representations to achieve this goal. |
| Outcome: | The proposed model performs well on the Image-Chat task and humans prefer it 47.7% of the time. |
Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs (D19-1)
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| Challenge: | Current approaches extract portions of web text as input to Sequence-to-Sequence models . a problem is generating relevant knowledge from noisy and redundant input such as webpages . |
| Approach: | They propose to restructure free text into local knowledge graphs that are linearized into sequences . they propose to encode the graph as a sequence and then linearize it into a structured sequence . |
| Outcome: | The proposed method compresses web search information and reduces redundancy . it can be used for open-domain question answering and multi-document summarization tasks . |
ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations (2020.acl-main)
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| Challenge: | Existing models for sentence simplification are focused on a single transformation, such as lexical paraphrasing or splitting. |
| Approach: | They propose a dataset for assessing sentence simplification in English using a crowdsourced multi-reference corpus. |
| Outcome: | The proposed dataset shows that it captures characteristics of simplicity better than other datasets. |
Generating Fact Checking Briefs (2020.emnlp-main)
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Angela Fan, Aleksandra Piktus, Fabio Petroni, Guillaume Wenzek, Marzieh Saeidi, Andreas Vlachos, Antoine Bordes, Sebastian Riedel
| Challenge: | Existing work has framed fact checking as classification, often supported by a claim as input. |
| Approach: | They propose to use natural language briefs to increase the accuracy of fact checking . they show that QABriefer increases the accuracy by 10% while QABries reduce time . |
| Outcome: | The proposed model increases the accuracy of crowdworkers by 10% while reducing the time required by 20%. |
Training Millions of Personalized Dialogue Agents (D18-1)
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| Challenge: | Current dialogue systems fail at being engaging for users when trained end-to-end without relying on proactive reengaging scripted strategies. |
| Approach: | They propose a dataset that provides 5 million personas and 700 million person-based dialogues. |
| Outcome: | The proposed dataset provides 5 million personas and 700 million person-based dialogues. |
MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases (2022.lrec-1)
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| Challenge: | MUSS trains strong models using sentence-level paraphrase data instead of labeled simplification data. |
| Approach: | They propose a multilingual unsupervised sentence simplification system that does not require labeled simplification data. |
| Outcome: | The proposed model outperforms the previous best supervised models on English, French, and Spanish benchmarks despite not using labeled simplification data. |
Learning from Dialogue after Deployment: Feed Yourself, Chatbot! (P19-1)
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| Challenge: | a majority of conversations a dialogue agent sees over its lifetime occur after it has already been trained and deployed, leaving a vast store of potential training signal untapped. |
| Approach: | They propose a self-feeding chatbot that extracts new training examples from conversations it participates in. |
| Outcome: | The proposed chatbot extracts training examples from conversations it participates in and predicts user satisfaction in its responses. |
Controllable Sentence Simplification (2020.lrec-1)
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| Challenge: | Text simplification is often considered an all-purpose generic task where the same simplifications are suitable for all but multiple audiences can benefit from simplified text in different ways. |
| Approach: | They propose a controllable simplification model that provides explicit control on simplification systems based on Sequence-to-Sequence models. |
| Outcome: | The proposed model outperforms standard models on simplification benchmarks. |