Sujith Ravi


2021

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SoDA: On-device Conversational Slot Extraction
Sujith Ravi | Zornitsa Kozareva
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

We propose a novel on-device neural sequence labeling model which uses embedding-free projections and character information to construct compact word representations to learn a sequence model using a combination of bidirectional LSTM with self-attention and CRF. Unlike typical dialog models that rely on huge, complex neural network architectures and large-scale pre-trained Transformers to achieve state-of-the-art results, our method achieves comparable results to BERT and even outperforms its smaller variant DistilBERT on conversational slot extraction tasks. Our method is faster than BERT models while achieving significant model size reduction–our model requires 135x and 81x fewer model parameters than BERT and DistilBERT, respectively. We conduct experiments on multiple conversational datasets and show significant improvements over existing methods including recent on-device models. Experimental results and ablation studies also show that our neural models preserve tiny memory footprint necessary to operate on smart devices, while still maintaining high performance.

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Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021)
Zornitsa Kozareva | Sujith Ravi | Andreas Vlachos | Priyanka Agrawal | André Martins
Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021)

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ProFormer: Towards On-Device LSH Projection Based Transformers
Chinnadhurai Sankar | Sujith Ravi | Zornitsa Kozareva
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

At the heart of text based neural models lay word representations, which are powerful but occupy a lot of memory making it challenging to deploy to devices with memory constraints such as mobile phones, watches and IoT. To surmount these challenges, we introduce ProFormer – a projection based transformer architecture that is faster and lighter making it suitable to deploy to memory constraint devices and preserve user privacy. We use LSH projection layer to dynamically generate word representations on-the-fly without embedding lookup tables leading to significant memory footprint reduction from O(V.d) to O(T), where V is the vocabulary size, d is the embedding dimension size and T is the dimension of the LSH projection representation.We also propose a local projection attention (LPA) layer, which uses self-attention to transform the input sequence of N LSH word projections into a sequence of N/K representations reducing the computations quadratically by O(Kˆ2). We evaluate ProFormer on multiple text classification tasks and observed improvements over prior state-of-the-art on-device approaches for short text classification and comparable performance for long text classification tasks. ProFormer is also competitive with other popular but highly resource-intensive approaches like BERT and even outperforms small-sized BERT variants with significant resource savings – reduces the embedding memory footprint from 92.16 MB to 1.7 KB and requires 16x less computation overhead, which is very impressive making it the fastest and smallest on-device model.

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On-Device Text Representations Robust To Misspellings via Projections
Chinnadhurai Sankar | Sujith Ravi | Zornitsa Kozareva
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Recently, there has been a strong interest in developing natural language applications that live on personal devices such as mobile phones, watches and IoT with the objective to preserve user privacy and have low memory. Advances in Locality-Sensitive Hashing (LSH)-based projection networks have demonstrated state-of-the-art performance in various classification tasks without explicit word (or word-piece) embedding lookup tables by computing on-the-fly text representations. In this paper, we show that the projection based neural classifiers are inherently robust to misspellings and perturbations of the input text. We empirically demonstrate that the LSH projection based classifiers are more robust to common misspellings compared to BiLSTMs (with both word-piece & word-only tokenization) and fine-tuned BERT based methods. When subject to misspelling attacks, LSH projection based classifiers had a small average accuracy drop of 2.94% across multiple classifications tasks, while the fine-tuned BERT model accuracy had a significant drop of 11.44%.

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Efficiently Summarizing Text and Graph Encodings of Multi-Document Clusters
Ramakanth Pasunuru | Mengwen Liu | Mohit Bansal | Sujith Ravi | Markus Dreyer
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

This paper presents an efficient graph-enhanced approach to multi-document summarization (MDS) with an encoder-decoder Transformer model. This model is based on recent advances in pre-training both encoder and decoder on very large text data (Lewis et al., 2019), and it incorporates an efficient encoding mechanism (Beltagy et al., 2020) that avoids the quadratic memory growth typical for traditional Transformers. We show that this powerful combination not only scales to large input documents commonly found when summarizing news clusters; it also enables us to process additional input in the form of auxiliary graph representations, which we derive from the multi-document clusters. We present a mechanism to incorporate such graph information into the encoder-decoder model that was pre-trained on text only. Our approach leads to significant improvements on the Multi-News dataset, overall leading to an average 1.8 ROUGE score improvement over previous work (Li et al., 2020). We also show improvements in a transfer-only setup on the DUC-2004 dataset. The graph encodings lead to summaries that are more abstractive. Human evaluation shows that they are also more informative and factually more consistent with their input documents.

2020

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Proceedings of the Fourth Workshop on Structured Prediction for NLP
Priyanka Agrawal | Zornitsa Kozareva | Julia Kreutzer | Gerasimos Lampouras | André Martins | Sujith Ravi | Andreas Vlachos
Proceedings of the Fourth Workshop on Structured Prediction for NLP

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GoEmotions: A Dataset of Fine-Grained Emotions
Dorottya Demszky | Dana Movshovitz-Attias | Jeongwoo Ko | Alan Cowen | Gaurav Nemade | Sujith Ravi
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to detecting harmful online behavior. Advancement in this area can be improved using large-scale datasets with a fine-grained typology, adaptable to multiple downstream tasks. We introduce GoEmotions, the largest manually annotated dataset of 58k English Reddit comments, labeled for 27 emotion categories or Neutral. We demonstrate the high quality of the annotations via Principal Preserved Component Analysis. We conduct transfer learning experiments with existing emotion benchmarks to show that our dataset generalizes well to other domains and different emotion taxonomies. Our BERT-based model achieves an average F1-score of .46 across our proposed taxonomy, leaving much room for improvement.

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Low-Dimensional Hyperbolic Knowledge Graph Embeddings
Ines Chami | Adva Wolf | Da-Cheng Juan | Frederic Sala | Sujith Ravi | Christopher Ré
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Knowledge graph (KG) embeddings learn low- dimensional representations of entities and relations to predict missing facts. KGs often exhibit hierarchical and logical patterns which must be preserved in the embedding space. For hierarchical data, hyperbolic embedding methods have shown promise for high-fidelity and parsimonious representations. However, existing hyperbolic embedding methods do not account for the rich logical patterns in KGs. In this work, we introduce a class of hyperbolic KG embedding models that simultaneously capture hierarchical and logical patterns. Our approach combines hyperbolic reflections and rotations with attention to model complex relational patterns. Experimental results on standard KG benchmarks show that our method improves over previous Euclidean- and hyperbolic-based efforts by up to 6.1% in mean reciprocal rank (MRR) in low dimensions. Furthermore, we observe that different geometric transformations capture different types of relations while attention- based transformations generalize to multiple relations. In high dimensions, our approach yields new state-of-the-art MRRs of 49.6% on WN18RR and 57.7% on YAGO3-10.

2019

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ProSeqo: Projection Sequence Networks for On-Device Text Classification
Zornitsa Kozareva | Sujith Ravi
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We propose a novel on-device sequence model for text classification using recurrent projections. Our model ProSeqo uses dynamic recurrent projections without the need to store or look up any pre-trained embeddings. This results in fast and compact neural networks that can perform on-device inference for complex short and long text classification tasks. We conducted exhaustive evaluation on multiple text classification tasks. Results show that ProSeqo outperformed state-of-the-art neural and on-device approaches for short text classification tasks such as dialog act and intent prediction. To the best of our knowledge, ProSeqo is the first on-device long text classification neural model. It achieved comparable results to previous neural approaches for news article, answers and product categorization, while preserving small memory footprint and maintaining high accuracy.

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PRADO: Projection Attention Networks for Document Classification On-Device
Prabhu Kaliamoorthi | Sujith Ravi | Zornitsa Kozareva
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Recently, there has been a great interest in the development of small and accurate neural networks that run entirely on devices such as mobile phones, smart watches and IoT. This enables user privacy, consistent user experience and low latency. Although a wide range of applications have been targeted from wake word detection to short text classification, yet there are no on-device networks for long text classification. We propose a novel projection attention neural network PRADO that combines trainable projections with attention and convolutions. We evaluate our approach on multiple large document text classification tasks. Our results show the effectiveness of the trainable projection model in finding semantically similar phrases and reaching high performance while maintaining compact size. Using this approach, we train tiny neural networks just 200 Kilobytes in size that improve over prior CNN and LSTM models and achieve near state of the art performance on multiple long document classification tasks. We also apply our model for transfer learning, show its robustness and ability to further improve the performance in limited data scenarios.

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Transferable Neural Projection Representations
Chinnadhurai Sankar | Sujith Ravi | Zornitsa Kozareva
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Neural word representations are at the core of many state-of-the-art natural language processing models. A widely used approach is to pre-train, store and look up word or character embedding matrices. While useful, such representations occupy huge memory making it hard to deploy on-device and often do not generalize to unknown words due to vocabulary pruning. In this paper, we propose a skip-gram based architecture coupled with Locality-Sensitive Hashing (LSH) projections to learn efficient dynamically computable representations. Our model does not need to store lookup tables as representations are computed on-the-fly and require low memory footprint. The representations can be trained in an unsupervised fashion and can be easily transferred to other NLP tasks. For qualitative evaluation, we analyze the nearest neighbors of the word representations and discover semantically similar words even with misspellings. For quantitative evaluation, we plug our transferable projections into a simple LSTM and run it on multiple NLP tasks and show how our transferable projections achieve better performance compared to prior work.

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Proceedings of the Third Workshop on Structured Prediction for NLP
Andre Martins | Andreas Vlachos | Zornitsa Kozareva | Sujith Ravi | Gerasimos Lampouras | Vlad Niculae | Julia Kreutzer
Proceedings of the Third Workshop on Structured Prediction for NLP

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Deep Reinforcement Learning For Modeling Chit-Chat Dialog With Discrete Attributes
Chinnadhurai Sankar | Sujith Ravi
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

Open domain dialog systems face the challenge of being repetitive and producing generic responses. In this paper, we demonstrate that by conditioning the response generation on interpretable discrete dialog attributes and composed attributes, it helps improve the model perplexity and results in diverse and interesting non-redundant responses. We propose to formulate the dialog attribute prediction as a reinforcement learning (RL) problem and use policy gradients methods to optimize utterance generation using long-term rewards. Unlike existing RL approaches which formulate the token prediction as a policy, our method reduces the complexity of the policy optimization by limiting the action space to dialog attributes, thereby making the policy optimization more practical and sample efficient. We demonstrate this with experimental and human evaluations.

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On-device Structured and Context Partitioned Projection Networks
Sujith Ravi | Zornitsa Kozareva
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

A challenging problem in on-device text classification is to build highly accurate neural models that can fit in small memory footprint and have low latency. To address this challenge, we propose an on-device neural network SGNN++ which dynamically learns compact projection vectors from raw text using structured and context-dependent partition projections. We show that this results in accelerated inference and performance improvements. We conduct extensive evaluation on multiple conversational tasks and languages such as English, Japanese, Spanish and French. Our SGNN++ model significantly outperforms all baselines, improves upon existing on-device neural models and even surpasses RNN, CNN and BiLSTM models on dialog act and intent prediction. Through a series of ablation studies we show the impact of the partitioned projections and structured information leading to 10% improvement. We study the impact of the model size on accuracy and introduce quatization-aware training for SGNN++ to further reduce the model size while preserving the same quality. Finally, we show fast inference on mobile phones.

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A2N: Attending to Neighbors for Knowledge Graph Inference
Trapit Bansal | Da-Cheng Juan | Sujith Ravi | Andrew McCallum
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

State-of-the-art models for knowledge graph completion aim at learning a fixed embedding representation of entities in a multi-relational graph which can generalize to infer unseen entity relationships at test time. This can be sub-optimal as it requires memorizing and generalizing to all possible entity relationships using these fixed representations. We thus propose a novel attention-based method to learn query-dependent representation of entities which adaptively combines the relevant graph neighborhood of an entity leading to more accurate KG completion. The proposed method is evaluated on two benchmark datasets for knowledge graph completion, and experimental results show that the proposed model performs competitively or better than existing state-of-the-art, including recent methods for explicit multi-hop reasoning. Qualitative probing offers insight into how the model can reason about facts involving multiple hops in the knowledge graph, through the use of neighborhood attention.

2018

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Self-Governing Neural Networks for On-Device Short Text Classification
Sujith Ravi | Zornitsa Kozareva
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applications. Yet, one of the biggest challenges is running these complex networks on devices such as mobile phones or smart watches with tiny memory footprint and low computational capacity. We propose on-device Self-Governing Neural Networks (SGNNs), which learn compact projection vectors with local sensitive hashing. The key advantage of SGNNs over existing work is that they surmount the need for pre-trained word embeddings and complex networks with huge parameters. We conduct extensive evaluation on dialog act classification and show significant improvement over state-of-the-art results. Our findings show that SGNNs are effective at capturing low-dimensional semantic text representations, while maintaining high accuracy.

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Self-Governing Neural Networks for On-Device Short Text Classification
Sujith Ravi | Zornitsa Kozareva
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applications. Yet, one of the biggest challenges is running these complex networks on devices such as mobile phones or smart watches with tiny memory footprint and low computational capacity. We propose on-device Self-Governing Neural Networks (SGNNs), which learn compact projection vectors with local sensitive hashing. The key advantage of SGNNs over existing work is that they surmount the need for pre-trained word embeddings and complex networks with huge parameters. We conduct extensive evaluation on dialog act classification and show significant improvement over state-of-the-art results. Our findings show that SGNNs are effective at capturing low-dimensional semantic text representations, while maintaining high accuracy.

2016

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Conversational Flow in Oxford-style Debates
Justine Zhang | Ravi Kumar | Sujith Ravi | Cristian Danescu-Niculescu-Mizil
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Parallel Algorithms for Unsupervised Tagging
Sujith Ravi | Sergei Vassilivitskii | Vibhor Rastogi
Transactions of the Association for Computational Linguistics, Volume 2

We propose a new method for unsupervised tagging that finds minimal models which are then further improved by Expectation Maximization training. In contrast to previous approaches that rely on manually specified and multi-step heuristics for model minimization, our approach is a simple greedy approximation algorithm DMLC (Distributed-Minimum-Label-Cover) that solves this objective in a single step. We extend the method and show how to efficiently parallelize the algorithm on modern parallel computing platforms while preserving approximation guarantees. The new method easily scales to large data and grammar sizes, overcoming the memory bottleneck in previous approaches. We demonstrate the power of the new algorithm by evaluating on various sequence labeling tasks: Part-of-Speech tagging for multiple languages (including low-resource languages), with complete and incomplete dictionaries, and supertagging, a complex sequence labeling task, where the grammar size alone can grow to millions of entries. Our results show that for all of these settings, our method achieves state-of-the-art scalable performance that yields high quality tagging outputs.

2013

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Scalable Decipherment for Machine Translation via Hash Sampling
Sujith Ravi
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Summarization Through Submodularity and Dispersion
Anirban Dasgupta | Ravi Kumar | Sujith Ravi
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Revisiting the Predictability of Language: Response Completion in Social Media
Bo Pang | Sujith Ravi
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Proceedings of the First Workshop on Multilingual Modeling
Jagadeesh Jagarlamudi | Sujith Ravi | Xiaojun Wan | Hal Daume III
Proceedings of the First Workshop on Multilingual Modeling

2011

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Unsupervised Name Ambiguity Resolution Using A Generative Model
Zornitsa Kozareva | Sujith Ravi
Proceedings of the First workshop on Unsupervised Learning in NLP

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Semantic Role Labeling Without Treebanks?
Stephen Boxwell | Chris Brew | Jason Baldridge | Dennis Mehay | Sujith Ravi
Proceedings of 5th International Joint Conference on Natural Language Processing

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Deciphering Foreign Language
Sujith Ravi | Kevin Knight
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Bayesian Inference for Zodiac and Other Homophonic Ciphers
Sujith Ravi | Kevin Knight
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Bayesian Inference for Finite-State Transducers
David Chiang | Jonathan Graehl | Kevin Knight | Adam Pauls | Sujith Ravi
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Minimized Models and Grammar-Informed Initialization for Supertagging with Highly Ambiguous Lexicons
Sujith Ravi | Jason Baldridge | Kevin Knight
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Fast, Greedy Model Minimization for Unsupervised Tagging
Sujith Ravi | Ashish Vaswani | Kevin Knight | David Chiang
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Squibs: Does GIZA++ Make Search Errors?
Sujith Ravi | Kevin Knight
Computational Linguistics, Volume 36, Issue 3 - September 2010

2009

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Minimized Models for Unsupervised Part-of-Speech Tagging
Sujith Ravi | Kevin Knight
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Learning Phoneme Mappings for Transliteration without Parallel Data
Sujith Ravi | Kevin Knight
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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A New Objective Function for Word Alignment
Tugba Bodrumlu | Kevin Knight | Sujith Ravi
Proceedings of the Workshop on Integer Linear Programming for Natural Language Processing

2008

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Attacking Decipherment Problems Optimally with Low-Order N-gram Models
Sujith Ravi | Kevin Knight
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

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Automatic Prediction of Parser Accuracy
Sujith Ravi | Kevin Knight | Radu Soricut
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing