Prasad Tadepalli


2020

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On the Sub-layer Functionalities of Transformer Decoder
Yilin Yang | Longyue Wang | Shuming Shi | Prasad Tadepalli | Stefan Lee | Zhaopeng Tu
Findings of the Association for Computational Linguistics: EMNLP 2020

There have been significant efforts to interpret the encoder of Transformer-based encoder-decoder architectures for neural machine translation (NMT); meanwhile, the decoder remains largely unexamined despite its critical role. During translation, the decoder must predict output tokens by considering both the source-language text from the encoder and the target-language prefix produced in previous steps. In this work, we study how Transformer-based decoders leverage information from the source and target languages – developing a universal probe task to assess how information is propagated through each module of each decoder layer. We perform extensive experiments on three major translation datasets (WMT En-De, En-Fr, and En-Zh). Our analysis provides insight on when and where decoders leverage different sources. Based on these insights, we demonstrate that the residual feed-forward module in each Transformer decoder layer can be dropped with minimal loss of performance – a significant reduction in computation and number of parameters, and consequently a significant boost to both training and inference speed.

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Relation Extraction with Explanation
Hamed Shahbazi | Xiaoli Fern | Reza Ghaeini | Prasad Tadepalli
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recent neural models for relation extraction with distant supervision alleviate the impact of irrelevant sentences in a bag by learning importance weights for the sentences. Efforts thus far have focused on improving extraction accuracy but little is known about their explanability. In this work we annotate a test set with ground-truth sentence-level explanations to evaluate the quality of explanations afforded by the relation extraction models. We demonstrate that replacing the entity mentions in the sentences with their fine-grained entity types not only enhances extraction accuracy but also improves explanation. We also propose to automatically generate “distractor” sentences to augment the bags and train the model to ignore the distractors. Evaluations on the widely used FB-NYT dataset show that our methods achieve new state-of-the-art accuracy while improving model explanability.

2019

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Description-Based Zero-shot Fine-Grained Entity Typing
Rasha Obeidat | Xiaoli Fern | Hamed Shahbazi | Prasad Tadepalli
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)

Fine-grained Entity typing (FGET) is the task of assigning a fine-grained type from a hierarchy to entity mentions in the text. As the taxonomy of types evolves continuously, it is desirable for an entity typing system to be able to recognize novel types without additional training. This work proposes a zero-shot entity typing approach that utilizes the type description available from Wikipedia to build a distributed semantic representation of the types. During training, our system learns to align the entity mentions and their corresponding type representations on the known types. At test time, any new type can be incorporated into the system given its Wikipedia descriptions. We evaluate our approach on FIGER, a public benchmark entity tying dataset. Because the existing test set of FIGER covers only a small portion of the fine-grained types, we create a new test set by manually annotating a portion of the noisy training data. Our experiments demonstrate the effectiveness of the proposed method in recognizing novel types that are not present in the training data.

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Saliency Learning: Teaching the Model Where to Pay Attention
Reza Ghaeini | Xiaoli Fern | Hamed Shahbazi | Prasad Tadepalli
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)

Deep learning has emerged as a compelling solution to many NLP tasks with remarkable performances. However, due to their opacity, such models are hard to interpret and trust. Recent work on explaining deep models has introduced approaches to provide insights toward the model’s behaviour and predictions, which are helpful for assessing the reliability of the model’s predictions. However, such methods do not improve the model’s reliability. In this paper, we aim to teach the model to make the right prediction for the right reason by providing explanation training and ensuring the alignment of the model’s explanation with the ground truth explanation. Our experimental results on multiple tasks and datasets demonstrate the effectiveness of the proposed method, which produces more reliable predictions while delivering better results compared to traditionally trained models.

2018

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Joint Neural Entity Disambiguation with Output Space Search
Hamed Shahbazi | Xiaoli Fern | Reza Ghaeini | Chao Ma | Rasha Mohammad Obeidat | Prasad Tadepalli
Proceedings of the 27th International Conference on Computational Linguistics

In this paper, we present a novel model for entity disambiguation that combines both local contextual information and global evidences through Limited Discrepancy Search (LDS). Given an input document, we start from a complete solution constructed by a local model and conduct a search in the space of possible corrections to improve the local solution from a global view point. Our search utilizes a heuristic function to focus more on the least confident local decisions and a pruning function to score the global solutions based on their local fitness and the global coherences among the predicted entities. Experimental results on CoNLL 2003 and TAC 2010 benchmarks verify the effectiveness of our model.

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Dependent Gated Reading for Cloze-Style Question Answering
Reza Ghaeini | Xiaoli Fern | Hamed Shahbazi | Prasad Tadepalli
Proceedings of the 27th International Conference on Computational Linguistics

We present a novel deep learning architecture to address the cloze-style question answering task. Existing approaches employ reading mechanisms that do not fully exploit the interdependency between the document and the query. In this paper, we propose a novel dependent gated reading bidirectional GRU network (DGR) to efficiently model the relationship between the document and the query during encoding and decision making. Our evaluation shows that DGR obtains highly competitive performance on well-known machine comprehension benchmarks such as the Children’s Book Test (CBT-NE and CBT-CN) and Who DiD What (WDW, Strict and Relaxed). Finally, we extensively analyze and validate our model by ablation and attention studies.

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Event Detection with Neural Networks: A Rigorous Empirical Evaluation
Walker Orr | Prasad Tadepalli | Xiaoli Fern
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Detecting events and classifying them into predefined types is an important step in knowledge extraction from natural language texts. While the neural network models have generally led the state-of-the-art, the differences in performance between different architectures have not been rigorously studied. In this paper we present a novel GRU-based model that combines syntactic information along with temporal structure through an attention mechanism. We show that it is competitive with other neural network architectures through empirical evaluations under different random initializations and training-validation-test splits of ACE2005 dataset.

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Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference
Reza Ghaeini | Xiaoli Fern | Prasad Tadepalli
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Deep learning models have achieved remarkable success in natural language inference (NLI) tasks. While these models are widely explored, they are hard to interpret and it is often unclear how and why they actually work. In this paper, we take a step toward explaining such deep learning based models through a case study on a popular neural model for NLI. In particular, we propose to interpret the intermediate layers of NLI models by visualizing the saliency of attention and LSTM gating signals. We present several examples for which our methods are able to reveal interesting insights and identify the critical information contributing to the model decisions.

2016

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Event Nugget Detection with Forward-Backward Recurrent Neural Networks
Reza Ghaeini | Xiaoli Fern | Liang Huang | Prasad Tadepalli
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2014

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Prune-and-Score: Learning for Greedy Coreference Resolution
Chao Ma | Janardhan Rao Doppa | J. Walker Orr | Prashanth Mannem | Xiaoli Fern | Tom Dietterich | Prasad Tadepalli
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2010

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Learning Rules from Incomplete Examples: A Pragmatic Approach
Janardhan Rao Doppa | Mohammad NasrEsfahani | Mohammad Sorower | Thomas G. Dietterich | Xiaoli Fern | Prasad Tadepalli
Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading