Vivek Srikumar


2022

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Right for the Right Reason Evidence Extraction for Trustworthy Tabular Reasoning
Vivek Gupta | Shuo Zhang | Alakananda Vempala | Yujie He | Temma Choji | Vivek Srikumar
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

When pre trained contextualized embedding based models developed for unstructured data are adapted for structured tabular data they perform admirably However recent probing studies show that these models use spurious correlations and often predict inference labels by focusing on false evidence or ignoring it altogether To study this issue we introduce the task of Trustworthy Tabular Reasoning where a model needs to extract evidence to be used for reasoning in addition to predicting the label As a case study we propose a two stage sequential prediction approach which includes an evidence extraction and an inference stage First we crowdsource evidence row labels and develop several unsupervised and supervised evidence extraction strategies for InfoTabS a tabular NLI benchmark Our evidence extraction strategy outperforms earlier baselines On the downstream tabular inference task using only the automatically extracted evidence as the premise our approach outperforms prior benchmarks

2020

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Structured Tuning for Semantic Role Labeling
Tao Li | Parth Anand Jawale | Martha Palmer | Vivek Srikumar
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recent neural network-driven semantic role labeling (SRL) systems have shown impressive improvements in F1 scores. These improvements are due to expressive input representations, which, at least at the surface, are orthogonal to knowledge-rich constrained decoding mechanisms that helped linear SRL models. Introducing the benefits of structure to inform neural models presents a methodological challenge. In this paper, we present a structured tuning framework to improve models using softened constraints only at training time. Our framework leverages the expressiveness of neural networks and provides supervision with structured loss components. We start with a strong baseline (RoBERTa) to validate the impact of our approach, and show that our framework outperforms the baseline by learning to comply with declarative constraints. Additionally, our experiments with smaller training sizes show that we can achieve consistent improvements under low-resource scenarios.

2019

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Preparing SNACS for Subjects and ObjectsSNACS for Subjects and Objects
Adi Shalev | Jena D. Hwang | Nathan Schneider | Vivek Srikumar | Omri Abend | Ari Rappoport
Proceedings of the First International Workshop on Designing Meaning Representations

Research on adpositions and possessives in multiple languages has led to a small inventory of general-purpose meaning classes that disambiguate tokens. Importantly, that work has argued for a principled separation of the semantic role in a scene from the function coded by morphosyntax. Here, we ask whether this approach can be generalized beyond adpositions and possessives to cover all scene participantsincluding subjects and objectsdirectly, without reference to a frame lexicon. We present new guidelines for English and the results of an interannotator agreement study.

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Augmenting Neural Networks with First-order Logic
Tao Li | Vivek Srikumar
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Today, the dominant paradigm for training neural networks involves minimizing task loss on a large dataset. Using world knowledge to inform a model, and yet retain the ability to perform end-to-end training remains an open question. In this paper, we present a novel framework for introducing declarative knowledge to neural network architectures in order to guide training and prediction. Our framework systematically compiles logical statements into computation graphs that augment a neural network without extra learnable parameters or manual redesign. We evaluate our modeling strategy on three tasks : machine comprehension, natural language inference, and text chunking. Our experiments show that knowledge-augmented networks can strongly improve over baselines, especially in low-data regimes.

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Observing Dialogue in Therapy : Categorizing and Forecasting Behavioral Codes
Jie Cao | Michael Tanana | Zac Imel | Eric Poitras | David Atkins | Vivek Srikumar
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Automatically analyzing dialogue can help understand and guide behavior in domains such as counseling, where interactions are largely mediated by conversation. In this paper, we study modeling behavioral codes used to asses a psychotherapy treatment style called Motivational Interviewing (MI), which is effective for addressing substance abuse and related problems. Specifically, we address the problem of providing real-time guidance to therapists with a dialogue observer that (1) categorizes therapist and client MI behavioral codes and, (2) forecasts codes for upcoming utterances to help guide the conversation and potentially alert the therapist. For both tasks, we define neural network models that build upon recent successes in dialogue modeling. Our experiments demonstrate that our models can outperform several baselines for both tasks. We also report the results of a careful analysis that reveals the impact of the various network design tradeoffs for modeling therapy dialogue.

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On the Limits of Learning to Actively Learn Semantic Representations
Omri Koshorek | Gabriel Stanovsky | Yichu Zhou | Vivek Srikumar | Jonathan Berant
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

One of the goals of natural language understanding is to develop models that map sentences into meaning representations. However, training such models requires expensive annotation of complex structures, which hinders their adoption. Learning to actively-learn(LTAL) is a recent paradigm for reducing the amount of labeled data by learning a policy that selects which samples should be labeled. In this work, we examine LTAL for learning semantic representations, such as QA-SRL. We show that even an oracle policy that is allowed to pick examples that maximize performance on the test set (and constitutes an upper bound on the potential of LTAL), does not substantially improve performance compared to a random policy. We investigate factors that could explain this finding and show that a distinguishing characteristic of successful applications of LTAL is the interaction between optimization and the oracle policy selection process. In successful applications of LTAL, the examples selected by the oracle policy do not substantially depend on the optimization procedure, while in our setup the stochastic nature of optimization strongly affects the examples selected by the oracle. We conclude that the current applicability of LTAL for improving data efficiency in learning semantic meaning representations is limited.

2018

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Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension
Shusen Liu | Tao Li | Zhimin Li | Vivek Srikumar | Valerio Pascucci | Peer-Timo Bremer
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Neural networks models have gained unprecedented popularity in natural language processing due to their state-of-the-art performance and the flexible end-to-end training scheme. Despite their advantages, the lack of interpretability hinders the deployment and refinement of the models. In this work, we present a flexible visualization library for creating customized visual analytic environments, in which the user can investigate and interrogate the relationships among the input, the model internals (i.e., attention), and the output predictions, which in turn shed light on the model decision-making process.

2017

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An Algebra for Feature Extraction
Vivek Srikumar
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Though feature extraction is a necessary first step in statistical NLP, it is often seen as a mere preprocessing step. Yet, it can dominate computation time, both during training, and especially at deployment. In this paper, we formalize feature extraction from an algebraic perspective. Our formalization allows us to define a message passing algorithm that can restructure feature templates to be more computationally efficient. We show via experiments on text chunking and relation extraction that this restructuring does indeed speed up feature extraction in practice by reducing redundant computation.

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Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing
Kai-Wei Chang | Ming-Wei Chang | Vivek Srikumar | Alexander M. Rush
Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing

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Double Trouble : The Problem of Construal in Semantic Annotation of Adpositions
Jena D. Hwang | Archna Bhatia | Na-Rae Han | Tim O’Gorman | Vivek Srikumar | Nathan Schneider
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

We consider the semantics of prepositions, revisiting a broad-coverage annotation scheme used for annotating all 4,250 preposition tokens in a 55,000 word corpus of English. Attempts to apply the scheme to adpositions and case markers in other languages, as well as some problematic cases in English, have led us to reconsider the assumption that an adposition’s lexical contribution is equivalent to the role / relation that it mediates. Our proposal is to embrace the potential for construal in adposition use, expressing such phenomena directly at the token level to manage complexity and avoid sense proliferation. We suggest a framework to represent both the scene role and the adposition’s lexical function so they can be annotated at scalesupporting automatic, statistical processing of domain-general languageand discuss how this representation would allow for a simpler inventory of labels.

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Integer Linear Programming formulations in Natural Language Processing
Dan Roth | Vivek Srikumar
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts

Making decisions in natural language processing problems often involves assigning values to sets of interdependent variables where the expressive dependency structure can influence, or even dictate what assignments are possible. This setting includes a broad range of structured prediction problems such as semantic role labeling, named entity and relation recognition, co-reference resolution, dependency parsing and semantic parsing. The setting is also appropriate for cases that may require making global decisions that involve multiple components, possibly pre-designed or pre-learned, as in event recognition and analysis, summarization, paraphrasing, textual entailment and question answering. In all these cases, it is natural to formulate the decision problem as a constrained optimization problem, with an objective function that is composed of learned models, subject to domain or problem specific constraints. Over the last few years, starting with a couple of papers written by (Roth & Yih, 2004, 2005), dozens of papers have been using the Integer linear programming (ILP) formulation developed there, including several award-winning papers (e.g., (Martins, Smith, & Xing, 2009 ; Koo, Rush, Collins, Jaakkola, & Sontag., 2010 ; Berant, Dagan, & Goldberger, 2011)).This tutorial will present the key ingredients of ILP formulations of natural language processing problems, aiming at guiding readers through the key modeling steps, explaining the learning and inference paradigms and exemplifying these by providing examples from the literature. We will cover a range of topics, from the theoretical foundations of learning and inference with ILP models, to practical modeling guides, to software packages and applications. The goal of this tutorial is to introduce the computational framework to broader ACL community, motivate it as a generic framework for learning and inference in global NLP decision problems, present some of the key theoretical and practical issues involved and survey some of the existing applications of it as a way to promote further development of the framework and additional applications. We will also make connections with some of the hot topics in current NLP research and show how they can be used within the general framework proposed here. The tutorial will thus be useful for many of the senior and junior researchers that have interest in global decision problems in NLP, providing a concise overview of recent perspectives and research results.