Papers by Marjorie Freedman

9 papers
Perhaps PTLMs Should Go to School – A Task to Assess Open Book and Closed Book QA (2021.emnlp-main)

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Challenge: Taking the exam closed book, but having read the textbook, yields at best minor improvement (56%), suggesting that the PTLM may not have “understood” the textbook (or perhaps misundersttoo the questions).
Approach: They propose to use pre-trained language models to answer questions from introductory college textbooks and hundreds of true/false statements based on review questions written by the authors.
Outcome: The proposed task includes two college-level introductory texts in the social sciences (American Government 2e) and humanities (U.S. History).
RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation (2023.acl-long)

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Challenge: Existing approaches to personalized dialogue generation rely on dialogue data paired with user traits, profiles or persona description sentences.
Approach: They propose a hierarchical transformer retriever trained on dialogue domain data to perform personalized retrieval and a context-aware prefix encoder that fuses the retrieved information to the decoder more effectively.
Outcome: The proposed model generates more fluent and personalized responses under a suite of human and automatic metrics and is superior to state-of-the-art baselines on English Reddit conversations.
Machine-Assisted Script Curation (2021.naacl-demos)

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Challenge: Scripts have been of interest for encoding procedural knowledge and understanding stories for over 40 years . narrative descriptions often omit common knowledge .
Approach: They propose a machine-aided script creator that automates script creation with suggestions for event types, links to Wikidata, and sub-events that may have been forgotten.
Outcome: The proposed system automates portions of the script creation process with suggestions for event types, links to Wikidata, and sub-events that may have been forgotten.
ACQUIRED: A Dataset for Answering Counterfactual Questions In Real-Life Videos (2023.emnlp-main)

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Challenge: despite its importance, there are few datasets that cover multimodal counterfactual reasoning . a dataset focusing on this area is limited because of its limited coverage over synthetic environments .
Approach: They develop a video question answering dataset that provides questions on multimodal reasoning . they ask questions about counterfactual hypotheses over visual events .
Outcome: The proposed dataset shows a significant performance gap between models and humans . it provides questions that span physical, social, and temporal dimensions .
Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals (2022.acl-long)

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Challenge: Current machine learning methods are incapable of efficiently utilizing multimodal information.
Approach: They propose to use text-and-image alignment to improve machine learning's performance on multimodal event sequencing.
Outcome: The proposed models perform significantly worse than humans on multimodal event sequencing than humans.
GAIA: A Fine-grained Multimedia Knowledge Extraction System (2020.acl-demos)

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Challenge: Open source knowledge extraction tools are used for many real-world applications, but there is no comprehensive system for KE.
Approach: They propose a multimedia knowledge extraction system that takes multimedia data from various sources and languages as input and creates a coherent, structured knowledge base.
Outcome: The system achieves top performance at the recent NIST TAC SM-KBP2019 evaluation.
When ACE met KBP: End-to-End Evaluation of Knowledge Base Population with Component-level Annotation (L18-1)

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Challenge: Automating constructing a Knowledge Base from unstructured text is a goal of natural language processing.
Approach: They propose a method to evaluate a Knowledge Base population from unstructured text . they propose bootstrap resampling to provide statistical significance to the results .
Outcome: The proposed method uses component-level annotations to evaluate Cold Start KBP . it also uses bootstrap resampling to provide statistical significance to the results reported .
Remember what you did so you know what to do next (2023.findings-emnlp)

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Challenge: Existing studies have shown large language models (LLMs) to be poor fit for a simulated robot to achieve 30 classes of goals.
Approach: They use the 6B parameter GPT-J language model to create a plan for a simulated robot to achieve 30 classes of goals in ScienceWorld.
Outcome: The proposed model outperforms the state-of-the-art by a factor of 1.4 when training on as many prior steps as will fit, and the results are 2.3x better than the state of the-art.
SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage (P19-3)

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Challenge: a new cross-lingual information retrieval system for low-resource languages is available in less-frequently-taught languages . a multilingual system can search for relevant information in a haystack of documents in swahili or Somali . human-driven approaches to this problem are complicated in 'low-resourced' languages aaron sagar: "the key role played by humans in triaging results is complicated"
Approach: They propose an end-to-end cross-lingual information retrieval system for low-resource languages . the system enables English speakers to search foreign language repositories using English queries . it summarizes the retrieved documents in English with respect to a particular information need .
Outcome: The proposed system achieves top performance in the most recent IARPA MATERIAL CLIR+summarization evaluations.

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