Mrinmaya Sachan


2020

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Knowledge Graph Embedding Compression
Mrinmaya Sachan
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Knowledge graph (KG) representation learning techniques that learn continuous embeddings of entities and relations in the KG have become popular in many AI applications. With a large KG, the embeddings consume a large amount of storage and memory. This is problematic and prohibits the deployment of these techniques in many real world settings. Thus, we propose an approach that compresses the KG embedding layer by representing each entity in the KG as a vector of discrete codes and then composes the embeddings from these codes. The approach can be trained end-to-end with simple modifications to any existing KG embedding technique. We evaluate the approach on various standard KG embedding evaluations and show that it achieves 50-1000x compression of embeddings with a minor loss in performance. The compressed embeddings also retain the ability to perform various reasoning tasks such as KG inference.

2019

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Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks
Mrinmaya Sachan | Avinava Dubey | Eduard H. Hovy | Tom M. Mitchell | Dan Roth | Eric P. Xing
Computational Linguistics, Volume 45, Issue 4 - December 2019

To ensure readability, text is often written and presented with due formatting. These text formatting devices help the writer to effectively convey the narrative. At the same time, these help the readers pick up the structure of the discourse and comprehend the conveyed information. There have been a number of linguistic theories on discourse structure of text. However, these theories only consider unformatted text. Multimedia text contains rich formatting features that can be leveraged for various NLP tasks. In this article, we study some of these discourse features in multimedia text and what communicative function they fulfill in the context. As a case study, we use these features to harvest structured subject knowledge of geometry from textbooks. We conclude that the discourse and text layout features provide information that is complementary to lexical semantic information. Finally, we show that the harvested structured knowledge can be used to improve an existing solver for geometry problems, making it more accurate as well as more explainable.

2018

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Self-Training for Jointly Learning to Ask and Answer Questions
Mrinmaya Sachan | Eric Xing
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Building curious machines that can answer as well as ask questions is an important challenge for AI. The two tasks of question answering and question generation are usually tackled separately in the NLP literature. At the same time, both require significant amounts of supervised data which is hard to obtain in many domains. To alleviate these issues, we propose a self-training method for jointly learning to ask as well as answer questions, leveraging unlabeled text along with labeled question answer pairs for learning. We evaluate our approach on four benchmark datasets: SQUAD, MS MARCO, WikiQA and TrecQA, and show significant improvements over a number of established baselines on both question answering and question generation tasks. We also achieved new state-of-the-art results on two competitive answer sentence selection tasks: WikiQA and TrecQA.

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Contextual Parameter Generation for Universal Neural Machine Translation
Emmanouil Antonios Platanios | Mrinmaya Sachan | Graham Neubig | Tom Mitchell
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We propose a simple modification to existing neural machine translation (NMT) models that enables using a single universal model to translate between multiple languages while allowing for language specific parameterization, and that can also be used for domain adaptation. Our approach requires no changes to the model architecture of a standard NMT system, but instead introduces a new component, the contextual parameter generator (CPG), that generates the parameters of the system (e.g., weights in a neural network). This parameter generator accepts source and target language embeddings as input, and generates the parameters for the encoder and the decoder, respectively. The rest of the model remains unchanged and is shared across all languages. We show how this simple modification enables the system to use monolingual data for training and also perform zero-shot translation. We further show it is able to surpass state-of-the-art performance for both the IWSLT-15 and IWSLT-17 datasets and that the learned language embeddings are able to uncover interesting relationships between languages.

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Standardized Tests as benchmarks for Artificial Intelligence
Mrinmaya Sachan | Minjoon Seo | Hannaneh Hajishirzi | Eric Xing
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

Standardized tests have recently been proposed as replacements to the Turing test as a driver for progress in AI (Clark, 2015). These include tests on understanding passages and stories and answering questions about them (Richardson et al., 2013; Rajpurkar et al., 2016a, inter alia), science question answering (Schoenick et al., 2016, inter alia), algebra word problems (Kushman et al., 2014, inter alia), geometry problems (Seo et al., 2015; Sachan et al., 2016), visual question answering (Antol et al., 2015), etc. Many of these tests require sophisticated understanding of the world, aiming to push the boundaries of AI. For this tutorial, we broadly categorize these tests into two categories: open domain tests such as reading comprehensions and elementary school tests where the goal is to find the support for an answer from the student curriculum, and closed domain tests such as intermediate level math and science tests (algebra, geometry, Newtonian physics problems, etc.). Unlike open domain tests, closed domain tests require the system to have significant domain knowledge and reasoning capabilities. For example, geometry questions typically involve a number of geometry primitives (lines, quadrilaterals, circles, etc) and require students to use axioms and theorems of geometry (Pythagoras theorem, alternating angles, etc) to solve them. These closed domains often have a formal logical basis and the question can be mapped to a formal language by semantic parsing. The formal question representation can then provided as an input to an expert system to solve the question.

2017

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Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks
Mrinmaya Sachan | Eric Xing
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

Humans as well as animals are good at imitation. Inspired by this, the learning by demonstration view of machine learning learns to perform a task from detailed example demonstrations. In this paper, we introduce the task of question answering using natural language demonstrations where the question answering system is provided with detailed demonstrative solutions to questions in natural language. As a case study, we explore the task of learning to solve geometry problems using demonstrative solutions available in textbooks. We collect a new dataset of demonstrative geometry solutions from textbooks and explore approaches that learn to interpret these demonstrations as well as to use these interpretations to solve geometry problems. Our approaches show improvements over the best previously published system for solving geometry problems.

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From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems
Mrinmaya Sachan | Kumar Dubey | Eric Xing
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Textbooks are rich sources of information. Harvesting structured knowledge from textbooks is a key challenge in many educational applications. As a case study, we present an approach for harvesting structured axiomatic knowledge from math textbooks. Our approach uses rich contextual and typographical features extracted from raw textbooks. It leverages the redundancy and shared ordering across multiple textbooks to further refine the harvested axioms. These axioms are then parsed into rules that are used to improve the state-of-the-art in solving geometry problems.

2016

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Easy Questions First? A Case Study on Curriculum Learning for Question Answering
Mrinmaya Sachan | Eric Xing
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Learning Concept Taxonomies from Multi-modal Data
Hao Zhang | Zhiting Hu | Yuntian Deng | Mrinmaya Sachan | Zhicheng Yan | Eric Xing
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Science Question Answering using Instructional Materials
Mrinmaya Sachan | Kumar Dubey | Eric Xing
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Machine Comprehension using Rich Semantic Representations
Mrinmaya Sachan | Eric Xing
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2015

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Learning Answer-Entailing Structures for Machine Comprehension
Mrinmaya Sachan | Kumar Dubey | Eric Xing | Matthew Richardson
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)

2013

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Identifying Metaphorical Word Use with Tree Kernels
Dirk Hovy | Shashank Srivastava | Sujay Kumar Jauhar | Mrinmaya Sachan | Kartik Goyal | Huying Li | Whitney Sanders | Eduard Hovy
Proceedings of the First Workshop on Metaphor in NLP

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A Structured Distributional Semantic Model : Integrating Structure with Semantics
Kartik Goyal | Sujay Kumar Jauhar | Huiying Li | Mrinmaya Sachan | Shashank Srivastava | Eduard Hovy
Proceedings of the Workshop on Continuous Vector Space Models and their Compositionality

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A Structured Distributional Semantic Model for Event Co-reference
Kartik Goyal | Sujay Kumar Jauhar | Huiying Li | Mrinmaya Sachan | Shashank Srivastava | Eduard Hovy
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2011

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Using Text Reviews for Product Entity Completion
Mrinmaya Sachan | Tanveer Faruquie | L. V. Subramaniam | Mukesh Mohania
Proceedings of 5th International Joint Conference on Natural Language Processing