Marina Danilevsky


2021

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SemEval-2021 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS)
Nancy X. R. Wang | Diwakar Mahajan | Marina Danilevsky | Sara Rosenthal
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

Understanding tables is an important and relevant task that involves understanding table structure as well as being able to compare and contrast information within cells. In this paper, we address this challenge by presenting a new dataset and tasks that addresses this goal in a shared task in SemEval 2020 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS). Our dataset contains 981 manually-generated tables and an auto-generated dataset of 1980 tables providing over 180K statement and over 16M evidence annotations. SEM-TAB-FACTS featured two sub-tasks. In sub-task A, the goal was to determine if a statement is supported, refuted or unknown in relation to a table. In sub-task B, the focus was on identifying the specific cells of a table that provide evidence for the statement. 69 teams signed up to participate in the task with 19 successful submissions to subtask A and 12 successful submissions to subtask B. We present our results and main findings from the competition.

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Development of an Enterprise-Grade Contract Understanding System
Arvind Agarwal | Laura Chiticariu | Poornima Chozhiyath Raman | Marina Danilevsky | Diman Ghazi | Ankush Gupta | Shanmukha Guttula | Yannis Katsis | Rajasekar Krishnamurthy | Yunyao Li | Shubham Mudgal | Vitobha Munigala | Nicholas Phan | Dhaval Sonawane | Sneha Srinivasan | Sudarshan R. Thitte | Mitesh Vasa | Ramiya Venkatachalam | Vinitha Yaski | Huaiyu Zhu
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers

Contracts are arguably the most important type of business documents. Despite their significance in business, legal contract review largely remains an arduous, expensive and manual process. In this paper, we describe TECUS: a commercial system designed and deployed for contract understanding and used by a wide range of enterprise users for the past few years. We reflect on the challenges and design decisions when building TECUS. We also summarize the data science life cycle of TECUS and share lessons learned.

2020

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A Survey of the State of Explainable AI for Natural Language Processing
Marina Danilevsky | Kun Qian | Ranit Aharonov | Yannis Katsis | Ban Kawas | Prithviraj Sen
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

Recent years have seen important advances in the quality of state-of-the-art models, but this has come at the expense of models becoming less interpretable. This survey presents an overview of the current state of Explainable AI (XAI), considered within the domain of Natural Language Processing (NLP). We discuss the main categorization of explanations, as well as the various ways explanations can be arrived at and visualized. We detail the operations and explainability techniques currently available for generating explanations for NLP model predictions, to serve as a resource for model developers in the community. Finally, we point out the current gaps and encourage directions for future work in this important research area.

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Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification
Prithviraj Sen | Marina Danilevsky | Yunyao Li | Siddhartha Brahma | Matthias Boehm | Laura Chiticariu | Rajasekar Krishnamurthy
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Interpretability of predictive models is becoming increasingly important with growing adoption in the real-world. We present RuleNN, a neural network architecture for learning transparent models for sentence classification. The models are in the form of rules expressed in first-order logic, a dialect with well-defined, human-understandable semantics. More precisely, RuleNN learns linguistic expressions (LE) built on top of predicates extracted using shallow natural language understanding. Our experimental results show that RuleNN outperforms statistical relational learning and other neuro-symbolic methods, and performs comparably with black-box recurrent neural networks. Our user studies confirm that the learned LEs are explainable and capture domain semantics. Moreover, allowing domain experts to modify LEs and instill more domain knowledge leads to human-machine co-creation of models with better performance.

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Active Learning for BERT: An Empirical Study
Liat Ein-Dor | Alon Halfon | Ariel Gera | Eyal Shnarch | Lena Dankin | Leshem Choshen | Marina Danilevsky | Ranit Aharonov | Yoav Katz | Noam Slonim
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Real world scenarios present a challenge for text classification, since labels are usually expensive and the data is often characterized by class imbalance. Active Learning (AL) is a ubiquitous paradigm to cope with data scarcity. Recently, pre-trained NLP models, and BERT in particular, are receiving massive attention due to their outstanding performance in various NLP tasks. However, the use of AL with deep pre-trained models has so far received little consideration. Here, we present a large-scale empirical study on active learning techniques for BERT-based classification, addressing a diverse set of AL strategies and datasets. We focus on practical scenarios of binary text classification, where the annotation budget is very small, and the data is often skewed. Our results demonstrate that AL can boost BERT performance, especially in the most realistic scenario in which the initial set of labeled examples is created using keyword-based queries, resulting in a biased sample of the minority class. We release our research framework, aiming to facilitate future research along the lines explored here.

2018

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DIMSIM: An Accurate Chinese Phonetic Similarity Algorithm Based on Learned High Dimensional Encoding
Min Li | Marina Danilevsky | Sara Noeman | Yunyao Li
Proceedings of the 22nd Conference on Computational Natural Language Learning

Phonetic similarity algorithms identify words and phrases with similar pronunciation which are used in many natural language processing tasks. However, existing approaches are designed mainly for Indo-European languages and fail to capture the unique properties of Chinese pronunciation. In this paper, we propose a high dimensional encoded phonetic similarity algorithm for Chinese, DIMSIM. The encodings are learned from annotated data to separately map initial and final phonemes into n-dimensional coordinates. Pinyin phonetic similarities are then calculated by aggregating the similarities of initial, final and tone. DIMSIM demonstrates a 7.5X improvement on mean reciprocal rank over the state-of-the-art phonetic similarity approaches.

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SystemT: Declarative Text Understanding for Enterprise
Laura Chiticariu | Marina Danilevsky | Yunyao Li | Frederick Reiss | Huaiyu Zhu
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

The rise of enterprise applications over unstructured and semi-structured documents poses new challenges to text understanding systems across multiple dimensions. We present SystemT, a declarative text understanding system that addresses these challenges and has been deployed in a wide range of enterprise applications. We highlight the design considerations and decisions behind SystemT in addressing the needs of the enterprise setting. We also summarize the impact of SystemT on business and education.

2016

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Multilingual Information Extraction with PolyglotIE
Alan Akbik | Laura Chiticariu | Marina Danilevsky | Yonas Kbrom | Yunyao Li | Huaiyu Zhu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

We present PolyglotIE, a web-based tool for developing extractors that perform Information Extraction (IE) over multilingual data. Our tool has two core features: First, it allows users to develop extractors against a unified abstraction that is shared across a large set of natural languages. This means that an extractor needs only be created once for one language, but will then run on multilingual data without any additional effort or language-specific knowledge on part of the user. Second, it embeds this abstraction as a set of views within a declarative IE system, allowing users to quickly create extractors using a mature IE query language. We present PolyglotIE as a hands-on demo in which users can experiment with creating extractors, execute them on multilingual text and inspect extraction results. Using the UI, we discuss the challenges and potential of using unified, crosslingual semantic abstractions as basis for downstream applications. We demonstrate multilingual IE for 9 languages from 4 different language groups: English, German, French, Spanish, Japanese, Chinese, Arabic, Russian and Hindi.

2015

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Generating High Quality Proposition Banks for Multilingual Semantic Role Labeling
Alan Akbik | Laura Chiticariu | Marina Danilevsky | Yunyao Li | Shivakumar Vaithyanathan | Huaiyu Zhu
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)