Papers by Diego Antognini
Rationalization through Concepts (2021.findings-acl)
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| Challenge: | Existing models that explain complex decisions are limited because of their lack of interpretability. |
| Approach: | They propose a model that extracts text snippets as concepts and infers which ones are described in the document. |
| Outcome: | The proposed model outperforms state-of-the-art methods trained on each aspect label independently. |
pNLP-Mixer: an Efficient all-MLP Architecture for Language (2023.acl-industry)
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| Challenge: | large pre-trained language models are impractical for on-device applications due to their size and inference cost. |
| Approach: | They propose an embedding-free MLP-Mixer model for on-device NLP that achieves high weight-efficiency thanks to a novel projection layer. |
| Outcome: | The proposed model beats state-of-the-art of tiny models by 97.8% on two datasets . it beats mBERT on MTOP and multiATIS, while using 170x less parameters . |
GameWikiSum: a Novel Large Multi-Document Summarization Dataset (2020.lrec-1)
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| Challenge: | Existing datasets contain only hundreds of samples, resulting in heavy reliance on hand-crafted features or manually annotated data. |
| Approach: | They propose a new domain-specific dataset for multi-document summarization that is 100 times larger than commonly used datasets. |
| Outcome: | The proposed dataset is 100 times larger than commonly used datasets and in another domain than news. |
Assistive Recipe Editing through Critiquing (2023.eacl-main)
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| Challenge: | Existing methods for generating recipes that satisfy dietary restrictions are inconsistent or incoherent and paired datasets are not available at scale. |
| Approach: | They propose to build a hierarchical denoising auto-encoder that edits recipes given ingredient-level critiques by interacting with the predicted ingredients. |
| Outcome: | The proposed model can more effectively edit recipes compared to strong language models and iteratively rewrites recipes to satisfy user feedback. |
Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization (D19-54)
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| Challenge: | Existing methods for summarizing documents rely on hand-crafted features or additional annotated data. |
| Approach: | They propose a method that makes use of two types of sentence embeddings . the method uses universal embeddable and domain-specific embeddible features . |
| Outcome: | The proposed method achieves competitive results on two types of summary, consisting of 665 bytes and 100 words. |
HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset (2020.lrec-1)
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| Challenge: | State-of-the-art deep learning-based recommender systems require large datasets to achieve their best performance. |
| Approach: | They propose to use TripAdvisor to build a large-scale hotel recommendation dataset with 50 million reviews. |
| Outcome: | The proposed dataset is the largest publicly available hotel recommendation dataset, based on TripAdvisor, with 50 million reviews. |
Unsupervised Term Extraction for Highly Technical Domains (2022.emnlp-industry)
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| Challenge: | Term extraction is an important task for knowledge discovery platforms because domain specific terms are the linguistic representation of domainspecific concepts. |
| Approach: | They propose a term extraction subsystem that uses an unsupervised annotator to generate training data to fine-tune transformer models. |
| Outcome: | The proposed system can generalize across domains while reducing latency and inference time while preserving the high performance of the existing system. |
Paraphrase and Solve: Exploring and Exploiting the Impact of Surface Form on Mathematical Reasoning in Large Language Models (2024.naacl-long)
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| Challenge: | Despite the impressive performance of large-scale language models, their ability to reason through complex problems remains a bottleneck. |
| Approach: | They propose a method which diversifies reasoning paths from specific surface forms of the problem to improve mathematical reasoning performance. |
| Outcome: | The proposed approach improves mathematical reasoning performance over vanilla self-consistency, especially for problems initially deemed unsolvable. |
Extracting Text Representations for Terms and Phrases in Technical Domains (2023.acl-industry)
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| Challenge: | Large pre-trained language models are extensively used in modern NLP systems. |
| Approach: | They propose an unsupervised approach to encoding using character-based models and pre-trained sentence encoders to reconstruct large pre-trained embedding matrices. |
| Outcome: | The proposed approach matches the quality of sentence encoders in technical domains and is 5 times smaller and up to 10 times faster on high-end GPUs. |