Papers by Marzieh Saeidi
Interpretation of Natural Language Rules in Conversational Machine Reading (D18-1)
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Marzieh Saeidi, Max Bartolo, Patrick Lewis, Sameer Singh, Tim Rocktäschel, Mike Sheldon, Guillaume Bouchard, Sebastian Riedel
| Challenge: | Existing work on question answering problems requires the reading of text because it contains a recipe to derive an answer together with the reader’s background knowledge. |
| Approach: | They formalise a task and develop a crowd-sourcing strategy to collect 37k task instances based on real-world rules and crowd-generated questions and scenarios. |
| Outcome: | The proposed task is based on 37k task instances based in real-world rules and crowd-generated questions and scenarios. |
Cross-Policy Compliance Detection via Question Answering (2021.emnlp-main)
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| Challenge: | Policy compliance detection is the task of ensuring that a scenario conforms to a policy. |
| Approach: | They propose to decompose policy compliance detection into question answering . they propose to use an existing dataset to augment expert annotations . |
| Outcome: | The proposed approach improves accuracy in cross-policy setups, especially when policies are unseen in training. |
Prompt-free and Efficient Few-shot Learning with Language Models (2022.acl-long)
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Rabeeh Karimi Mahabadi, Luke Zettlemoyer, James Henderson, Lambert Mathias, Marzieh Saeidi, Veselin Stoyanov, Majid Yazdani
| Challenge: | Existing methods for few-shot fine-tuning of pretrained language models require carefully engineered prompts and verbalizers to convert inputs into a cloze-format that the PLM can score. |
| Approach: | They propose a method for few-shot fine-tuning of pretrained language models that uses task-specific adapters instead of manually engineered prompts and verbalizers. |
| Outcome: | The proposed method outperforms existing state-of-the-art methods on a wide range of few shot NLP tasks. |
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%. |
RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question (2023.findings-acl)
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Alireza Mohammadshahi, Thomas Scialom, Majid Yazdani, Pouya Yanki, Angela Fan, James Henderson, Marzieh Saeidi
| Challenge: | Existing metrics for evaluating the quality of automatically generated questions are expensive and penalise valid questions that may not have high lexical or semantic similarity to the reference questions. |
| Approach: | They propose a question-answering and span scorer metric based on the answerability of the candidate question given the context. |
| Outcome: | The proposed metric has higher correlation with human judgment without relying on the reference question. |
Automated Fact-Checking in Dialogue: Are Specialized Models Needed? (2023.emnlp-main)
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| Challenge: | Prior work has shown that typical fact-checking models struggle with claims made in conversation. |
| Approach: | They propose to fine-tune models for dialogue on conversational data to improve performance on typical fact-checking. |
| Outcome: | The proposed models perform better on stand-alone claims than state-of-the-art models for dialogue while maintaining their performance on standalone claim. |
TimelineQA: A Benchmark for Question Answering over Timelines (2023.findings-acl)
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Wang-Chiew Tan, Jane Dwivedi-Yu, Yuliang Li, Lambert Mathias, Marzieh Saeidi, Jing Nathan Yan, Alon Halevy
| Challenge: | Existing question answering techniques for lifelogs do not provide accurate answers . augmented reality glasses have led to the creation of personal assistants . |
| Approach: | They propose to use a benchmark to query lifelogs to find out what happened in real life . they find that extractive QA systems out-perform retrieval-augmented QA techniques . |
| Outcome: | The proposed method outperforms state-of-the-art retrieval-augmented QA systems in atomic queries and multi-hop queries. |
Database reasoning over text (2021.acl-long)
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| Challenge: | Existing models cannot handle database queries such as “List/Count all female athletes who were born in 20th century”. |
| Approach: | They propose a modular architecture to answer database-style queries over multiple spans from text and aggregate them at scale. |
| Outcome: | The proposed architecture scales to databases containing thousands of facts whereas current models are limited by how many facts can be encoded. |