Karthik Narasimhan


2022

pdf bib
CARETS: A Consistency And Robustness Evaluative Test Suite for VQA
Carlos Jimenez | Olga Russakovsky | Karthik Narasimhan
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We introduce CARETS, a systematic test suite to measure consistency and robustness of modern VQA models through a series of six fine-grained capability tests. In contrast to existing VQA test sets, CARETS features balanced question generation to create pairs of instances to test models, with each pair focusing on a specific capability such as rephrasing, logical symmetry or image obfuscation. We evaluate six modern VQA systems on CARETS and identify several actionable weaknesses in model comprehension, especially with concepts such as negation, disjunction, or hypernym invariance. Interestingly, even the most sophisticated models are sensitive to aspects such as swapping the order of terms in a conjunction or varying the number of answer choices mentioned in the question. We release CARETS to be used as an extensible tool for evaluating multi-modal model robustness.

pdf bib
Proceedings of the First Workshop on Learning with Natural Language Supervision
Jacob Andreas | Karthik Narasimhan | Aida Nematzadeh
Proceedings of the First Workshop on Learning with Natural Language Supervision

2021

pdf bib
Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents
Shunyu Yao | Karthik Narasimhan | Matthew Hausknecht
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Text-based games simulate worlds and interact with players using natural language. Recent work has used them as a testbed for autonomous language-understanding agents, with the motivation being that understanding the meanings of words or semantics is a key component of how humans understand, reason, and act in these worlds. However, it remains unclear to what extent artificial agents utilize semantic understanding of the text. To this end, we perform experiments to systematically reduce the amount of semantic information available to a learning agent. Surprisingly, we find that an agent is capable of achieving high scores even in the complete absence of language semantics, indicating that the currently popular experimental setup and models may be poorly designed to understand and leverage game texts. To remedy this deficiency, we propose an inverse dynamics decoder to regularize the representation space and encourage exploration, which shows improved performance on several games including Zork I. We discuss the implications of our findings for designing future agents with stronger semantic understanding.

pdf bib
Universal Adversarial Attacks with Natural Triggers for Text Classification
Liwei Song | Xinwei Yu | Hsuan-Tung Peng | Karthik Narasimhan
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Recent work has demonstrated the vulnerability of modern text classifiers to universal adversarial attacks, which are input-agnostic sequences of words added to text processed by classifiers. Despite being successful, the word sequences produced in such attacks are often ungrammatical and can be easily distinguished from natural text. We develop adversarial attacks that appear closer to natural English phrases and yet confuse classification systems when added to benign inputs. We leverage an adversarially regularized autoencoder (ARAE) to generate triggers and propose a gradient-based search that aims to maximize the downstream classifier’s prediction loss. Our attacks effectively reduce model accuracy on classification tasks while being less identifiable than prior models as per automatic detection metrics and human-subject studies. Our aim is to demonstrate that adversarial attacks can be made harder to detect than previously thought and to enable the development of appropriate defenses.

pdf bib
Improving Dialog Systems for Negotiation with Personality Modeling
Runzhe Yang | Jingxiao Chen | Karthik Narasimhan
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

In this paper, we explore the ability to model and infer personality types of opponents, predict their responses, and use this information to adapt a dialog agent’s high-level strategy in negotiation tasks. Inspired by the idea of incorporating a theory of mind (ToM) into machines, we introduce a probabilistic formulation to encapsulate the opponent’s personality type during both learning and inference. We test our approach on the CraigslistBargain dataset (He et al. 2018) and show that our method using ToM inference achieves a 20% higher dialog agreement rate compared to baselines on a mixed population of opponents. We also demonstrate that our model displays diverse negotiation behavior with different types of opponents.

pdf bib
Self-Attention Networks Can Process Bounded Hierarchical Languages
Shunyu Yao | Binghui Peng | Christos Papadimitriou | Karthik Narasimhan
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Despite their impressive performance in NLP, self-attention networks were recently proved to be limited for processing formal languages with hierarchical structure, such as Dyck-k, the language consisting of well-nested parentheses of k types. This suggested that natural language can be approximated well with models that are too weak for formal languages, or that the role of hierarchy and recursion in natural language might be limited. We qualify this implication by proving that self-attention networks can process Dyck-(k, D), the subset of Dyck-k with depth bounded by D, which arguably better captures the bounded hierarchical structure of natural language. Specifically, we construct a hard-attention network with D+1 layers and O(log k) memory size (per token per layer) that recognizes Dyck-(k, D), and a soft-attention network with two layers and O(log k) memory size that generates Dyck-(k, D). Experiments show that self-attention networks trained on Dyck-(k, D) generalize to longer inputs with near-perfect accuracy, and also verify the theoretical memory advantage of self-attention networks over recurrent networks.

2020

pdf bib
Keep CALM and Explore: Language Models for Action Generation in Text-based Games
Shunyu Yao | Rohan Rao | Matthew Hausknecht | Karthik Narasimhan
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Text-based games present a unique challenge for autonomous agents to operate in natural language and handle enormous action spaces. In this paper, we propose the Contextual Action Language Model (CALM) to generate a compact set of action candidates at each game state. Our key insight is to train language models on human gameplay, where people demonstrate linguistic priors and a general game sense for promising actions conditioned on game history. We combine CALM with a reinforcement learning agent which re-ranks the generated action candidates to maximize in-game rewards. We evaluate our approach using the Jericho benchmark, on games unseen by CALM during training. Our method obtains a 69% relative improvement in average game score over the previous state-of-the-art model. Surprisingly, on half of these games, CALM is competitive with or better than other models that have access to ground truth admissible actions. Code and data are available at https://github.com/princeton-nlp/calm-textgame.

pdf bib
Robust and Interpretable Grounding of Spatial References with Relation Networks
Tsung-Yen Yang | Andrew Lan | Karthik Narasimhan
Findings of the Association for Computational Linguistics: EMNLP 2020

Learning representations of spatial references in natural language is a key challenge in tasks like autonomous navigation and robotic manipulation. Recent work has investigated various neural architectures for learning multi-modal representations for spatial concepts. However, the lack of explicit reasoning over entities makes such approaches vulnerable to noise in input text or state observations. In this paper, we develop effective models for understanding spatial references in text that are robust and interpretable, without sacrificing performance. We design a text-conditioned relation network whose parameters are dynamically computed with a cross-modal attention module to capture fine-grained spatial relations between entities. This design choice provides interpretability of learned intermediate outputs. Experiments across three tasks demonstrate that our model achieves superior performance, with a 17% improvement in predicting goal locations and a 15% improvement in robustness compared to state-of-the-art systems.

pdf bib
Guiding Attention for Self-Supervised Learning with Transformers
Ameet Deshpande | Karthik Narasimhan
Findings of the Association for Computational Linguistics: EMNLP 2020

In this paper, we propose a simple and effective technique to allow for efficient self-supervised learning with bi-directional Transformers. Our approach is motivated by recent studies demonstrating that self-attention patterns in trained models contain a majority of non-linguistic regularities. We propose a computationally efficient auxiliary loss function to guide attention heads to conform to such patterns. Our method is agnostic to the actual pre-training objective and results in faster convergence of models as well as better performance on downstream tasks compared to the baselines, achieving state of the art results in low-resource settings. Surprisingly, we also find that linguistic properties of attention heads are not necessarily correlated with language modeling performance.

2018

pdf bib
Representation Learning for Grounded Spatial Reasoning
Michael Janner | Karthik Narasimhan | Regina Barzilay
Transactions of the Association for Computational Linguistics, Volume 6

The interpretation of spatial references is highly contextual, requiring joint inference over both language and the environment. We consider the task of spatial reasoning in a simulated environment, where an agent can act and receive rewards. The proposed model learns a representation of the world steered by instruction text. This design allows for precise alignment of local neighborhoods with corresponding verbalizations, while also handling global references in the instructions. We train our model with reinforcement learning using a variant of generalized value iteration. The model outperforms state-of-the-art approaches on several metrics, yielding a 45% reduction in goal localization error.

2017

pdf bib
Unsupervised Learning of Morphological Forests
Jiaming Luo | Karthik Narasimhan | Regina Barzilay
Transactions of the Association for Computational Linguistics, Volume 5

This paper focuses on unsupervised modeling of morphological families, collectively comprising a forest over the language vocabulary. This formulation enables us to capture edge-wise properties reflecting single-step morphological derivations, along with global distributional properties of the entire forest. These global properties constrain the size of the affix set and encourage formation of tight morphological families. The resulting objective is solved using Integer Linear Programming (ILP) paired with contrastive estimation. We train the model by alternating between optimizing the local log-linear model and the global ILP objective. We evaluate our system on three tasks: root detection, clustering of morphological families, and segmentation. Our experiments demonstrate that our model yields consistent gains in all three tasks compared with the best published results.

2016

pdf bib
Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge
Nicholas Locascio | Karthik Narasimhan | Eduardo DeLeon | Nate Kushman | Regina Barzilay
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

pdf bib
Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning
Karthik Narasimhan | Adam Yala | Regina Barzilay
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

pdf bib
Nonparametric Spherical Topic Modeling with Word Embeddings
Kayhan Batmanghelich | Ardavan Saeedi | Karthik Narasimhan | Sam Gershman
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2015

pdf bib
Language Understanding for Text-based Games using Deep Reinforcement Learning
Karthik Narasimhan | Tejas Kulkarni | Regina Barzilay
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

pdf bib
An Unsupervised Method for Uncovering Morphological Chains
Karthik Narasimhan | Regina Barzilay | Tommi Jaakkola
Transactions of the Association for Computational Linguistics, Volume 3

Most state-of-the-art systems today produce morphological analysis based only on orthographic patterns. In contrast, we propose a model for unsupervised morphological analysis that integrates orthographic and semantic views of words. We model word formation in terms of morphological chains, from base words to the observed words, breaking the chains into parent-child relations. We use log-linear models with morpheme and word-level features to predict possible parents, including their modifications, for each word. The limited set of candidate parents for each word render contrastive estimation feasible. Our model consistently matches or outperforms five state-of-the-art systems on Arabic, English and Turkish.

pdf bib
Machine Comprehension with Discourse Relations
Karthik Narasimhan | Regina Barzilay
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)

2014

pdf bib
Morphological Segmentation for Keyword Spotting
Karthik Narasimhan | Damianos Karakos | Richard Schwartz | Stavros Tsakalidis | Regina Barzilay
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)