Marjorie Freedman


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Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals
Te-Lin Wu | Alex Spangher | Pegah Alipoormolabashi | Marjorie Freedman | Ralph Weischedel | Nanyun Peng
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The ability to sequence unordered events is evidence of comprehension and reasoning about real world tasks/procedures. It is essential for applications such as task planning and multi-source instruction summarization.It often requires thorough understanding of temporal common sense and multimodal information, since these procedures are often conveyed by a combination of texts and images.While humans are capable of reasoning about and sequencing unordered procedural instructions, the extent to which the current machine learning methods possess such capability is still an open question.In this work, we benchmark models’ capability of reasoning over and sequencing unordered multimodal instructions by curating datasets from online instructional manuals and collecting comprehensive human annotations.We find current state-of-the-art models not only perform significantly worse than humans but also seem incapable of efficiently utilizing multimodal information.To improve machines’ performance on multimodal event sequencing, we propose sequence-aware pretraining techniques exploiting the sequential alignment properties of both texts and images, resulting in > 5% improvements on perfect match ratio.


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Machine-Assisted Script Curation
Manuel Ciosici | Joseph Cummings | Mitchell DeHaven | Alex Hedges | Yash Kankanampati | Dong-Ho Lee | Ralph Weischedel | Marjorie Freedman
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations

We describe Machine-Aided Script Curator (MASC), a system for human-machine collaborative script authoring. Scripts produced with MASC include (1) English descriptions of sub-events that comprise a larger, complex event; (2) event types for each of those events; (3) a record of entities expected to participate in multiple sub-events; and (4) temporal sequencing between the sub-events. MASC automates portions of the script creation process with suggestions for event types, links to Wikidata, and sub-events that may have been forgotten. We illustrate how these automations are useful to the script writer with a few case-study scripts.

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Perhaps PTLMs Should Go to School – A Task to Assess Open Book and Closed Book QA
Manuel Ciosici | Joe Cecil | Dong-Ho Lee | Alex Hedges | Marjorie Freedman | Ralph Weischedel
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Our goal is to deliver a new task and leaderboard to stimulate research on question answering and pre-trained language models (PTLMs) to understand a significant instructional document, e.g., an introductory college textbook or a manual. PTLMs have shown great success in many question-answering tasks, given significant supervised training, but much less so in zero-shot settings. We propose a new task that includes two college-level introductory texts in the social sciences (American Government 2e) and humanities (U.S. History), hundreds of true/false statements based on review questions written by the textbook authors, validation/development tests based on the first eight chapters of the textbooks, blind tests based on the remaining textbook chapters, and baseline results given state-of-the-art PTLMs. Since the questions are balanced, random performance should be ~50%. T5, fine-tuned with BoolQ achieves the same performance, suggesting that the textbook’s content is not pre-represented in the PTLM. Taking the exam closed book, but having read the textbook (i.e., adding the textbook to T5’s pre-training), yields at best minor improvement (56%), suggesting that the PTLM may not have “understood” the textbook (or perhaps misunderstood the questions). Performance is better (~60%) when the exam is taken open-book (i.e., allowing the machine to automatically retrieve a paragraph and use it to answer the question).

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Agenda Pushing in Email to Thwart Phishing
Hyundong Cho | Genevieve Bartlett | Marjorie Freedman
Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021)

In this work, we draw parallels between automatically responding to emails for combating social-engineering attacks and document-grounded response generation and lay out the blueprint of our approach. Phishing emails are longer than dialogue utterances and often contain multiple intents. Hence, we need to make decisions similar to those for document-grounded responses in deciding what parts of long text to use and how to address each intent to generate a knowledgeable multi-component response that pushes scammers towards agendas that aid in attribution and linking attacks. We propose , a hybrid system that uses customizable probabilistic finite state transducers to orchestrate pushing agendas coupled with neural dialogue systems that generate responses to unexpected prompts, as a promising solution to this end. We emphasize the need for this system by highlighting each component’s strengths and weaknesses and show how they complement each other.


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SEARCHER: Shared Embedding Architecture for Effective Retrieval
Joel Barry | Elizabeth Boschee | Marjorie Freedman | Scott Miller
Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)

We describe an approach to cross lingual information retrieval that does not rely on explicit translation of either document or query terms. Instead, both queries and documents are mapped into a shared embedding space where retrieval is performed. We discuss potential advantages of the approach in handling polysemy and synonymy. We present a method for training the model, and give details of the model implementation. We present experimental results for two cases: Somali-English and Bulgarian-English CLIR.

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GAIA: A Fine-grained Multimedia Knowledge Extraction System
Manling Li | Alireza Zareian | Ying Lin | Xiaoman Pan | Spencer Whitehead | Brian Chen | Bo Wu | Heng Ji | Shih-Fu Chang | Clare Voss | Daniel Napierski | Marjorie Freedman
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We present the first comprehensive, open source multimedia knowledge extraction system that takes a massive stream of unstructured, heterogeneous multimedia data from various sources and languages as input, and creates a coherent, structured knowledge base, indexing entities, relations, and events, following a rich, fine-grained ontology. Our system, GAIA, enables seamless search of complex graph queries, and retrieves multimedia evidence including text, images and videos. GAIA achieves top performance at the recent NIST TAC SM-KBP2019 evaluation. The system is publicly available at GitHub and DockerHub, with a narrated video that documents the system.


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SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage
Elizabeth Boschee | Joel Barry | Jayadev Billa | Marjorie Freedman | Thamme Gowda | Constantine Lignos | Chester Palen-Michel | Michael Pust | Banriskhem Kayang Khonglah | Srikanth Madikeri | Jonathan May | Scott Miller
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

With the increasing democratization of electronic media, vast information resources are available in less-frequently-taught languages such as Swahili or Somali. That information, which may be crucially important and not available elsewhere, can be difficult for monolingual English speakers to effectively access. In this paper we present an end-to-end cross-lingual information retrieval (CLIR) and summarization system for low-resource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed. The SARAL system achieved the top end-to-end performance in the most recent IARPA MATERIAL CLIR+summarization evaluations. Our demonstration system provides end-to-end open query retrieval and summarization capability, and presents the original source text or audio, speech transcription, and machine translation, for two low resource languages.

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Contextualized Cross-Lingual Event Trigger Extraction with Minimal Resources
Meryem M’hamdi | Marjorie Freedman | Jonathan May
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Event trigger extraction is an information extraction task of practical utility, yet it is challenging due to the difficulty of disambiguating word sense meaning. Previous approaches rely extensively on hand-crafted language-specific features and are applied mainly to English for which annotated datasets and Natural Language Processing (NLP) tools are available. However, the availability of such resources varies from one language to another. Recently, contextualized Bidirectional Encoder Representations from Transformers (BERT) models have established state-of-the-art performance for a variety of NLP tasks. However, there has not been much effort in exploring language transfer using BERT for event extraction. In this work, we treat event trigger extraction as a sequence tagging problem and propose a cross-lingual framework for training it without any hand-crafted features. We experiment with different flavors of transfer learning from high-resourced to low-resourced languages and compare the performance of different multilingual embeddings for event trigger extraction. Our results show that training in a multilingual setting outperforms language-specific models for both English and Chinese. Our work is the first to experiment with two event architecture variants in a cross-lingual setting, to show the effectiveness of contextualized embeddings obtained using BERT, and to explore and analyze its performance on Arabic.


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When ACE met KBP: End-to-End Evaluation of Knowledge Base Population with Component-level Annotation
Bonan Min | Marjorie Freedman | Roger Bock | Ralph Weischedel
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Learning Transferable Representation for Bilingual Relation Extraction via Convolutional Neural Networks
Bonan Min | Zhuolin Jiang | Marjorie Freedman | Ralph Weischedel
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Typically, relation extraction models are trained to extract instances of a relation ontology using only training data from a single language. However, the concepts represented by the relation ontology (e.g. ResidesIn, EmployeeOf) are language independent. The numbers of annotated examples available for a given ontology vary between languages. For example, there are far fewer annotated examples in Spanish and Japanese than English and Chinese. Furthermore, using only language-specific training data results in the need to manually annotate equivalently large amounts of training for each new language a system encounters. We propose a deep neural network to learn transferable, discriminative bilingual representation. Experiments on the ACE 2005 multilingual training corpus demonstrate that the joint training process results in significant improvement in relation classification performance over the monolingual counterparts. The learnt representation is discriminative and transferable between languages. When using 10% (25K English words, or 30K Chinese characters) of the training data, our approach results in doubling F1 compared to a monolingual baseline. We achieve comparable performance to the monolingual system trained with 250K English words (or 300K Chinese characters) With 50% of training data.

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Probabilistic Inference for Cold Start Knowledge Base Population with Prior World Knowledge
Bonan Min | Marjorie Freedman | Talya Meltzer
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Building knowledge bases (KB) automatically from text corpora is crucial for many applications such as question answering and web search. The problem is very challenging and has been divided into sub-problems such as mention and named entity recognition, entity linking and relation extraction. However, combining these components has shown to be under-constrained and often produces KBs with supersize entities and common-sense errors in relations (a person has multiple birthdates). The errors are difficult to resolve solely with IE tools but become obvious with world knowledge at the corpus level. By analyzing Freebase and a large text collection, we found that per-relation cardinality and the popularity of entities follow the power-law distribution favoring flat long tails with low-frequency instances. We present a probabilistic joint inference algorithm to incorporate this world knowledge during KB construction. Our approach yields state-of-the-art performance on the TAC Cold Start task, and 42% and 19.4% relative improvements in F1 over our baseline on Cold Start hop-1 and all-hop queries respectively.


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A Comparison of Event Representations in DEFT
Ann Bies | Zhiyi Song | Jeremy Getman | Joe Ellis | Justin Mott | Stephanie Strassel | Martha Palmer | Teruko Mitamura | Marjorie Freedman | Heng Ji | Tim O’Gorman
Proceedings of the Fourth Workshop on Events


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Extreme Extraction – Machine Reading in a Week
Marjorie Freedman | Lance Ramshaw | Elizabeth Boschee | Ryan Gabbard | Gary Kratkiewicz | Nicolas Ward | Ralph Weischedel
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Coreference for Learning to Extract Relations: Yes Virginia, Coreference Matters
Ryan Gabbard | Marjorie Freedman | Ralph Weischedel
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Language Use: What can it tell us?
Marjorie Freedman | Alex Baron | Vasin Punyakanok | Ralph Weischedel
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies


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Empirical Studies in Learning to Read
Marjorie Freedman | Edward Loper | Elizabeth Boschee | Ralph Weischedel
Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading


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Who is Who and What is What: Experiments in Cross-Document Co-Reference
Alex Baron | Marjorie Freedman
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing