Partha Talukdar

Also published as: Partha P. Talukdar, Partha Pratim Talukdar, Partha Pratim Talukdar


2022

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Overlap-based Vocabulary Generation Improves Cross-lingual Transfer Among Related Languages
Vaidehi Patil | Partha Talukdar | Sunita Sarawagi
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Pre-trained multilingual language models such as mBERT and XLM-R have demonstrated great potential for zero-shot cross-lingual transfer to low web-resource languages (LRL). However, due to limited model capacity, the large difference in the sizes of available monolingual corpora between high web-resource languages (HRL) and LRLs does not provide enough scope of co-embedding the LRL with the HRL, thereby affecting the downstream task performance of LRLs. In this paper, we argue that relatedness among languages in a language family along the dimension of lexical overlap may be leveraged to overcome some of the corpora limitations of LRLs. We propose Overlap BPE (OBPE), a simple yet effective modification to the BPE vocabulary generation algorithm which enhances overlap across related languages. Through extensive experiments on multiple NLP tasks and datasets, we observe that OBPE generates a vocabulary that increases the representation of LRLs via tokens shared with HRLs. This results in improved zero-shot transfer from related HRLs to LRLs without reducing HRL representation and accuracy. Unlike previous studies that dismissed the importance of token-overlap, we show that in the low-resource related language setting, token overlap matters. Synthetically reducing the overlap to zero can cause as much as a four-fold drop in zero-shot transfer accuracy.

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Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings
Kalpesh Krishna | Deepak Nathani | Xavier Garcia | Bidisha Samanta | Partha Talukdar
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Style transfer is the task of rewriting a sentence into a target style while approximately preserving content. While most prior literature assumes access to a large style-labelled corpus, recent work (Riley et al. 2021) has attempted “few-shot” style transfer using only 3-10 sentences at inference for style extraction. In this work we study a relevant low-resource setting: style transfer for languages where no style-labelled corpora are available. We notice that existing few-shot methods perform this task poorly, often copying inputs verbatim. We push the state-of-the-art for few-shot style transfer with a new method modeling the stylistic difference between paraphrases. When compared to prior work, our model achieves 2-3x better performance in formality transfer and code-mixing addition across seven languages. Moreover, our method is better at controlling the style transfer magnitude using an input scalar knob. We report promising qualitative results for several attribute transfer tasks (sentiment transfer, simplification, gender neutralization, text anonymization) all without retraining the model. Finally, we find model evaluation to be difficult due to the lack of datasets and metrics for many languages. To facilitate future research we crowdsource formality annotations for 4000 sentence pairs in four Indic languages, and use this data to design our automatic evaluations.

2021

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OKGIT: Open Knowledge Graph Link Prediction with Implicit Types
. Chandrahas | Partha Talukdar
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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MergeDistill: Merging Language Models using Pre-trained Distillation
Simran Khanuja | Melvin Johnson | Partha Talukdar
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Reordering Examples Helps during Priming-based Few-Shot Learning
Sawan Kumar | Partha Talukdar
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages Study
Yash Khemchandani | Sarvesh Mehtani | Vaidehi Patil | Abhijeet Awasthi | Partha Talukdar | Sunita Sarawagi
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)

Recent research in multilingual language models (LM) has demonstrated their ability to effectively handle multiple languages in a single model. This holds promise for low web-resource languages (LRL) as multilingual models can enable transfer of supervision from high resource languages to LRLs. However, incorporating a new language in an LM still remains a challenge, particularly for languages with limited corpora and in unseen scripts. In this paper we argue that relatedness among languages in a language family may be exploited to overcome some of the corpora limitations of LRLs, and propose RelateLM. We focus on Indian languages, and exploit relatedness along two dimensions: (1) script (since many Indic scripts originated from the Brahmic script), and (2) sentence structure. RelateLM uses transliteration to convert the unseen script of limited LRL text into the script of a Related Prominent Language (RPL) (Hindi in our case). While exploiting similar sentence structures, RelateLM utilizes readily available bilingual dictionaries to pseudo translate RPL text into LRL corpora. Experiments on multiple real-world benchmark datasets provide validation to our hypothesis that using a related language as pivot, along with transliteration and pseudo translation based data augmentation, can be an effective way to adapt LMs for LRLs, rather than direct training or pivoting through English.

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Question Answering Over Temporal Knowledge Graphs
Apoorv Saxena | Soumen Chakrabarti | Partha Talukdar
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)

Temporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by providing temporal scopes (start and end times) on each edge in the KG. While Question Answering over KG (KGQA) has received some attention from the research community, QA over Temporal KGs (Temporal KGQA) is a relatively unexplored area. Lack of broad coverage datasets has been another factor limiting progress in this area. We address this challenge by presenting CRONQUESTIONS, the largest known Temporal KGQA dataset, clearly stratified into buckets of structural complexity. CRONQUESTIONS expands the only known previous dataset by a factor of 340x. We find that various state-of-the-art KGQA methods fall far short of the desired performance on this new dataset. In response, we also propose CRONKGQA, a transformer-based solution that exploits recent advances in Temporal KG embeddings, and achieves performance superior to all baselines, with an increase of 120% in accuracy over the next best performing method. Through extensive experiments, we give detailed insights into the workings of CRONKGQA, as well as situations where significant further improvements appear possible. In addition to the dataset, we have released our code as well.

2020

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Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings
Apoorv Saxena | Aditay Tripathi | Partha Talukdar
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Knowledge Graphs (KG) are multi-relational graphs consisting of entities as nodes and relations among them as typed edges. Goal of the Question Answering over KG (KGQA) task is to answer natural language queries posed over the KG. Multi-hop KGQA requires reasoning over multiple edges of the KG to arrive at the right answer. KGs are often incomplete with many missing links, posing additional challenges for KGQA, especially for multi-hop KGQA. Recent research on multi-hop KGQA has attempted to handle KG sparsity using relevant external text, which isn’t always readily available. In a separate line of research, KG embedding methods have been proposed to reduce KG sparsity by performing missing link prediction. Such KG embedding methods, even though highly relevant, have not been explored for multi-hop KGQA so far. We fill this gap in this paper and propose EmbedKGQA. EmbedKGQA is particularly effective in performing multi-hop KGQA over sparse KGs. EmbedKGQA also relaxes the requirement of answer selection from a pre-specified neighborhood, a sub-optimal constraint enforced by previous multi-hop KGQA methods. Through extensive experiments on multiple benchmark datasets, we demonstrate EmbedKGQA’s effectiveness over other state-of-the-art baselines.

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A Re-evaluation of Knowledge Graph Completion Methods
Zhiqing Sun | Shikhar Vashishth | Soumya Sanyal | Partha Talukdar | Yiming Yang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs. A vast number of state-of-the-art KGC techniques have got published at top conferences in several research fields, including data mining, machine learning, and natural language processing. However, we notice that several recent papers report very high performance, which largely outperforms previous state-of-the-art methods. In this paper, we find that this can be attributed to the inappropriate evaluation protocol used by them and propose a simple evaluation protocol to address this problem. The proposed protocol is robust to handle bias in the model, which can substantially affect the final results. We conduct extensive experiments and report performance of several existing methods using our protocol. The reproducible code has been made publicly available.

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NILE : Natural Language Inference with Faithful Natural Language Explanations
Sawan Kumar | Partha Talukdar
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The recent growth in the popularity and success of deep learning models on NLP classification tasks has accompanied the need for generating some form of natural language explanation of the predicted labels. Such generated natural language (NL) explanations are expected to be faithful, i.e., they should correlate well with the model’s internal decision making. In this work, we focus on the task of natural language inference (NLI) and address the following question: can we build NLI systems which produce labels with high accuracy, while also generating faithful explanations of its decisions? We propose Natural-language Inference over Label-specific Explanations (NILE), a novel NLI method which utilizes auto-generated label-specific NL explanations to produce labels along with its faithful explanation. We demonstrate NILE’s effectiveness over previously reported methods through automated and human evaluation of the produced labels and explanations. Our evaluation of NILE also supports the claim that accurate systems capable of providing testable explanations of their decisions can be designed. We discuss the faithfulness of NILE’s explanations in terms of sensitivity of the decisions to the corresponding explanations. We argue that explicit evaluation of faithfulness, in addition to label and explanation accuracy, is an important step in evaluating model’s explanations. Further, we demonstrate that task-specific probes are necessary to establish such sensitivity.

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Syntax-Guided Controlled Generation of Paraphrases
Ashutosh Kumar | Kabir Ahuja | Raghuram Vadapalli | Partha Talukdar
Transactions of the Association for Computational Linguistics, Volume 8

Given a sentence (e.g., “I like mangoes”) and a constraint (e.g., sentiment flip), the goal of controlled text generation is to produce a sentence that adapts the input sentence to meet the requirements of the constraint (e.g., “I hate mangoes”). Going beyond such simple constraints, recent work has started exploring the incorporation of complex syntactic-guidance as constraints in the task of controlled paraphrase generation. In these methods, syntactic-guidance is sourced from a separate exemplar sentence. However, this prior work has only utilized limited syntactic information available in the parse tree of the exemplar sentence. We address this limitation in the paper and propose Syntax Guided Controlled Paraphraser (SGCP), an end-to-end framework for syntactic paraphrase generation. We find that Sgcp can generate syntax-conforming sentences while not compromising on relevance. We perform extensive automated and human evaluations over multiple real-world English language datasets to demonstrate the efficacy of Sgcp over state-of-the-art baselines. To drive future research, we have made Sgcp’s source code available.1

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Learning to Interact: An Adaptive Interaction Framework for Knowledge Graph Embeddings
. Chandrahas | Nilesh Agrawal | Partha Talukdar
Proceedings of the 17th International Conference on Natural Language Processing (ICON)

Knowledge Graph (KG) Embedding methods have been widely studied in the past few years and many methods have been proposed. These methods represent entities and relations in the KG as vectors in a vector space, trained to distinguish correct edges from the incorrect ones. For this distinction, simple functions of vectors’ dimensions, called interactions, are used. These interactions are used to calculate the candidate tail entity vector which is matched against all entities in the KG. However, for most of the existing methods, these interactions are fixed and manually specified. In this work, we propose an automated framework for discovering the interactions while training the KG Embeddings. The proposed method learns relevant interactions along with other parameters during training, allowing it to adapt to different datasets. Many of the existing methods can be seen as special cases of the proposed framework. We demonstrate the effectiveness of the proposed method on link prediction task by extensive experiments on multiple benchmark datasets.

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Inducing Interpretability in Knowledge Graph Embeddings
. Chandrahas | Tathagata Sengupta | Cibi Pragadeesh | Partha Talukdar
Proceedings of the 17th International Conference on Natural Language Processing (ICON)

We study the problem of inducing interpretability in Knowledge Graph (KG) embeddings. Learning KG embeddings has been an active area of research in the past few years, resulting in many different models. However, most of these methods do not address the interpretability (semantics) of individual dimensions of the learned embeddings. In this work, we study this problem and propose a method for inducing interpretability in KG embeddings using entity co-occurrence statistics. The proposed method significantly improves the interpretability, while maintaining comparable performance in other KG tasks.

2019

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CaRe: Open Knowledge Graph Embeddings
Swapnil Gupta | Sreyash Kenkre | Partha Talukdar
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Open Information Extraction (OpenIE) methods are effective at extracting (noun phrase, relation phrase, noun phrase) triples from text, e.g., (Barack Obama, took birth in, Honolulu). Organization of such triples in the form of a graph with noun phrases (NPs) as nodes and relation phrases (RPs) as edges results in the construction of Open Knowledge Graphs (OpenKGs). In order to use such OpenKGs in downstream tasks, it is often desirable to learn embeddings of the NPs and RPs present in the graph. Even though several Knowledge Graph (KG) embedding methods have been recently proposed, all of those methods have targeted Ontological KGs, as opposed to OpenKGs. Straightforward application of existing Ontological KG embedding methods to OpenKGs is challenging, as unlike Ontological KGs, OpenKGs are not canonicalized, i.e., a real-world entity may be represented using multiple nodes in the OpenKG, with each node corresponding to a different NP referring to the entity. For example, nodes with labels Barack Obama, Obama, and President Obama may refer to the same real-world entity Barack Obama. Even though canonicalization of OpenKGs has received some attention lately, output of such methods has not been used to improve OpenKG embed- dings. We fill this gap in the paper and propose Canonicalization-infused Representations (CaRe) for OpenKGs. Through extensive experiments, we observe that CaRe enables existing models to adapt to the challenges in OpenKGs and achieve substantial improvements for the link prediction task.

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Graph-based Deep Learning in Natural Language Processing
Shikhar Vashishth | Naganand Yadati | Partha Talukdar
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tutorial Abstracts

This tutorial aims to introduce recent advances in graph-based deep learning techniques such as Graph Convolutional Networks (GCNs) for Natural Language Processing (NLP). It provides a brief introduction to deep learning methods on non-Euclidean domains such as graphs and justifies their relevance in NLP. It then covers recent advances in applying graph-based deep learning methods for various NLP tasks, such as semantic role labeling, machine translation, relationship extraction, and many more.

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Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
Shikhar Vashishth | Manik Bhandari | Prateek Yadav | Piyush Rai | Chiranjib Bhattacharyya | Partha Talukdar
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Word embeddings have been widely adopted across several NLP applications. Most existing word embedding methods utilize sequential context of a word to learn its embedding. While there have been some attempts at utilizing syntactic context of a word, such methods result in an explosion of the vocabulary size. In this paper, we overcome this problem by proposing SynGCN, a flexible Graph Convolution based method for learning word embeddings. SynGCN utilizes the dependency context of a word without increasing the vocabulary size. Word embeddings learned by SynGCN outperform existing methods on various intrinsic and extrinsic tasks and provide an advantage when used with ELMo. We also propose SemGCN, an effective framework for incorporating diverse semantic knowledge for further enhancing learned word representations. We make the source code of both models available to encourage reproducible research.

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Relating Simple Sentence Representations in Deep Neural Networks and the Brain
Sharmistha Jat | Hao Tang | Partha Talukdar | Tom Mitchell
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

What is the relationship between sentence representations learned by deep recurrent models against those encoded by the brain? Is there any correspondence between hidden layers of these recurrent models and brain regions when processing sentences? Can these deep models be used to synthesize brain data which can then be utilized in other extrinsic tasks? We investigate these questions using sentences with simple syntax and semantics (e.g., The bone was eaten by the dog.). We consider multiple neural network architectures, including recently proposed ELMo and BERT. We use magnetoencephalography (MEG) brain recording data collected from human subjects when they were reading these simple sentences. Overall, we find that BERT’s activations correlate the best with MEG brain data. We also find that the deep network representation can be used to generate brain data from new sentences to augment existing brain data. To the best of our knowledge, this is the first work showing that the MEG brain recording when reading a word in a sentence can be used to distinguish earlier words in the sentence. Our exploration is also the first to use deep neural network representations to generate synthetic brain data and to show that it helps in improving subsequent stimuli decoding task accuracy.

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Zero-shot Word Sense Disambiguation using Sense Definition Embeddings
Sawan Kumar | Sharmistha Jat | Karan Saxena | Partha Talukdar
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Word Sense Disambiguation (WSD) is a long-standing but open problem in Natural Language Processing (NLP). WSD corpora are typically small in size, owing to an expensive annotation process. Current supervised WSD methods treat senses as discrete labels and also resort to predicting the Most-Frequent-Sense (MFS) for words unseen during training. This leads to poor performance on rare and unseen senses. To overcome this challenge, we propose Extended WSD Incorporating Sense Embeddings (EWISE), a supervised model to perform WSD by predicting over a continuous sense embedding space as opposed to a discrete label space. This allows EWISE to generalize over both seen and unseen senses, thus achieving generalized zero-shot learning. To obtain target sense embeddings, EWISE utilizes sense definitions. EWISE learns a novel sentence encoder for sense definitions by using WordNet relations and also ConvE, a recently proposed knowledge graph embedding method. We also compare EWISE against other sentence encoders pretrained on large corpora to generate definition embeddings. EWISE achieves new state-of-the-art WSD performance.

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Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation
Ashutosh Kumar | Satwik Bhattamishra | Manik Bhandari | Partha Talukdar
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)

Inducing diversity in the task of paraphrasing is an important problem in NLP with applications in data augmentation and conversational agents. Previous paraphrasing approaches have mainly focused on the issue of generating semantically similar paraphrases while paying little attention towards diversity. In fact, most of the methods rely solely on top-k beam search sequences to obtain a set of paraphrases. The resulting set, however, contains many structurally similar sentences. In this work, we focus on the task of obtaining highly diverse paraphrases while not compromising on paraphrasing quality. We provide a novel formulation of the problem in terms of monotone submodular function maximization, specifically targeted towards the task of paraphrasing. Additionally, we demonstrate the effectiveness of our method for data augmentation on multiple tasks such as intent classification and paraphrase recognition. In order to drive further research, we have made the source code available.

2018

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Towards Understanding the Geometry of Knowledge Graph Embeddings
Chandrahas | Aditya Sharma | Partha Talukdar
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embedding methods. These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space. Despite this popularity and effectiveness of KG embeddings in various tasks (e.g., link prediction), geometric understanding of such embeddings (i.e., arrangement of entity and relation vectors in vector space) is unexplored – we fill this gap in the paper. We initiate a study to analyze the geometry of KG embeddings and correlate it with task performance and other hyperparameters. To the best of our knowledge, this is the first study of its kind. Through extensive experiments on real-world datasets, we discover several insights. For example, we find that there are sharp differences between the geometry of embeddings learnt by different classes of KG embeddings methods. We hope that this initial study will inspire other follow-up research on this important but unexplored problem.

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Higher-order Relation Schema Induction using Tensor Factorization with Back-off and Aggregation
Madhav Nimishakavi | Manish Gupta | Partha Talukdar
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Relation Schema Induction (RSI) is the problem of identifying type signatures of arguments of relations from unlabeled text. Most of the previous work in this area have focused only on binary RSI, i.e., inducing only the subject and object type signatures per relation. However, in practice, many relations are high-order, i.e., they have more than two arguments and inducing type signatures of all arguments is necessary. For example, in the sports domain, inducing a schema win(WinningPlayer, OpponentPlayer, Tournament, Location) is more informative than inducing just win(WinningPlayer, OpponentPlayer). We refer to this problem as Higher-order Relation Schema Induction (HRSI). In this paper, we propose Tensor Factorization with Back-off and Aggregation (TFBA), a novel framework for the HRSI problem. To the best of our knowledge, this is the first attempt at inducing higher-order relation schemata from unlabeled text. Using the experimental analysis on three real world datasets we show how TFBA helps in dealing with sparsity and induce higher-order schemata.

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Dating Documents using Graph Convolution Networks
Shikhar Vashishth | Shib Sankar Dasgupta | Swayambhu Nath Ray | Partha Talukdar
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Document date is essential for many important tasks, such as document retrieval, summarization, event detection, etc. While existing approaches for these tasks assume accurate knowledge of the document date, this is not always available, especially for arbitrary documents from the Web. Document Dating is a challenging problem which requires inference over the temporal structure of the document. Prior document dating systems have largely relied on handcrafted features while ignoring such document-internal structures. In this paper, we propose NeuralDater, a Graph Convolutional Network (GCN) based document dating approach which jointly exploits syntactic and temporal graph structures of document in a principled way. To the best of our knowledge, this is the first application of deep learning for the problem of document dating. Through extensive experiments on real-world datasets, we find that NeuralDater significantly outperforms state-of-the-art baseline by 19% absolute (45% relative) accuracy points.

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ELDEN: Improved Entity Linking Using Densified Knowledge Graphs
Priya Radhakrishnan | Partha Talukdar | Vasudeva Varma
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in a Knowledge Graph (KG). Degree of connectivity of an entity in the KG directly affects an EL system’s ability to correctly link mentions in text to the entity in KG. This causes many EL systems to perform well for entities well connected to other entities in KG, bringing into focus the role of KG density in EL. In this paper, we propose Entity Linking using Densified Knowledge Graphs (ELDEN). ELDEN is an EL system which first densifies the KG with co-occurrence statistics from a large text corpus, and then uses the densified KG to train entity embeddings. Entity similarity measured using these trained entity embeddings result in improved EL. ELDEN outperforms state-of-the-art EL system on benchmark datasets. Due to such densification, ELDEN performs well for sparsely connected entities in the KG too. ELDEN’s approach is simple, yet effective. We have made ELDEN’s code and data publicly available.

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RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information
Shikhar Vashishth | Rishabh Joshi | Sai Suman Prayaga | Chiranjib Bhattacharyya | Partha Talukdar
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Distantly-supervised Relation Extraction (RE) methods train an extractor by automatically aligning relation instances in a Knowledge Base (KB) with unstructured text. In addition to relation instances, KBs often contain other relevant side information, such as aliases of relations (e.g., founded and co-founded are aliases for the relation founderOfCompany). RE models usually ignore such readily available side information. In this paper, we propose RESIDE, a distantly-supervised neural relation extraction method which utilizes additional side information from KBs for improved relation extraction. It uses entity type and relation alias information for imposing soft constraints while predicting relations. RESIDE employs Graph Convolution Networks (GCN) to encode syntactic information from text and improves performance even when limited side information is available. Through extensive experiments on benchmark datasets, we demonstrate RESIDE’s effectiveness. We have made RESIDE’s source code available to encourage reproducible research.

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AD3: Attentive Deep Document Dater
Swayambhu Nath Ray | Shib Sankar Dasgupta | Partha Talukdar
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Knowledge of the creation date of documents facilitates several tasks such as summarization, event extraction, temporally focused information extraction etc. Unfortunately, for most of the documents on the Web, the time-stamp metadata is either missing or can’t be trusted. Thus, predicting creation time from document content itself is an important task. In this paper, we propose Attentive Deep Document Dater (AD3), an attention-based neural document dating system which utilizes both context and temporal information in documents in a flexible and principled manner. We perform extensive experimentation on multiple real-world datasets to demonstrate the effectiveness of AD3 over neural and non-neural baselines.

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HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding
Shib Sankar Dasgupta | Swayambhu Nath Ray | Partha Talukdar
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Knowledge Graph (KG) embedding has emerged as an active area of research resulting in the development of several KG embedding methods. Relational facts in KG often show temporal dynamics, e.g., the fact (Cristiano_Ronaldo, playsFor, Manchester_United) is valid only from 2003 to 2009. Most of the existing KG embedding methods ignore this temporal dimension while learning embeddings of the KG elements. In this paper, we propose HyTE, a temporally aware KG embedding method which explicitly incorporates time in the entity-relation space by associating each timestamp with a corresponding hyperplane. HyTE not only performs KG inference using temporal guidance, but also predicts temporal scopes for relational facts with missing time annotations. Through extensive experimentation on temporal datasets extracted from real-world KGs, we demonstrate the effectiveness of our model over both traditional as well as temporal KG embedding methods.

2017

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KGEval: Accuracy Estimation of Automatically Constructed Knowledge Graphs
Prakhar Ojha | Partha Talukdar
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Automatic construction of large knowledge graphs (KG) by mining web-scale text datasets has received considerable attention recently. Estimating accuracy of such automatically constructed KGs is a challenging problem due to their size and diversity. This important problem has largely been ignored in prior research – we fill this gap and propose KGEval. KGEval uses coupling constraints to bind facts and crowdsources those few that can infer large parts of the graph. We demonstrate that the objective optimized by KGEval is submodular and NP-hard, allowing guarantees for our approximation algorithm. Through experiments on real-world datasets, we demonstrate that KGEval best estimates KG accuracy compared to other baselines, while requiring significantly lesser number of human evaluations.

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Speeding up Reinforcement Learning-based Information Extraction Training using Asynchronous Methods
Aditya Sharma | Zarana Parekh | Partha Talukdar
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

RLIE-DQN is a recently proposed Reinforcement Learning-based Information Extraction (IE) technique which is able to incorporate external evidence during the extraction process. RLIE-DQN trains a single agent sequentially, training on one instance at a time. This results in significant training slowdown which is undesirable. We leverage recent advances in parallel RL training using asynchronous methods and propose RLIE-A3C. RLIE-A3C trains multiple agents in parallel and is able to achieve upto 6x training speedup over RLIE-DQN, while suffering no loss in average accuracy.

2016

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Relation Schema Induction using Tensor Factorization with Side Information
Madhav Nimishakavi | Uday Singh Saini | Partha Talukdar
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

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An Entity-centric Approach for Overcoming Knowledge Graph Sparsity
Manjunath Hegde | Partha P. Talukdar
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Translation Invariant Word Embeddings
Kejun Huang | Matt Gardner | Evangelos Papalexakis | Christos Faloutsos | Nikos Sidiropoulos | Tom Mitchell | Partha P. Talukdar | Xiao Fu
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Knowledge Base Inference using Bridging Entities
Bhushan Kotnis | Pradeep Bansal | Partha P. Talukdar
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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A Compositional and Interpretable Semantic Space
Alona Fyshe | Leila Wehbe | Partha P. Talukdar | Brian Murphy | Tom M. Mitchell
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Incorporating Vector Space Similarity in Random Walk Inference over Knowledge Bases
Matt Gardner | Partha Talukdar | Jayant Krishnamurthy | Tom Mitchell
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Interpretable Semantic Vectors from a Joint Model of Brain- and Text- Based Meaning
Alona Fyshe | Partha P. Talukdar | Brian Murphy | Tom M. Mitchell
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Improving Learning and Inference in a Large Knowledge-Base using Latent Syntactic Cues
Matt Gardner | Partha Pratim Talukdar | Bryan Kisiel | Tom Mitchell
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Documents and Dependencies: an Exploration of Vector Space Models for Semantic Composition
Alona Fyshe | Brian Murphy | Partha Talukdar | Tom Mitchell
Proceedings of the Seventeenth Conference on Computational Natural Language Learning

2012

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Selecting Corpus-Semantic Models for Neurolinguistic Decoding
Brian Murphy | Partha Talukdar | Tom Mitchell
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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Learning Effective and Interpretable Semantic Models using Non-Negative Sparse Embedding
Brian Murphy | Partha Talukdar | Tom Mitchell
Proceedings of COLING 2012

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Metric Learning for Graph-Based Domain Adaptation
Paramveer Dhillon | Partha Talukdar | Koby Crammer
Proceedings of COLING 2012: Posters

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Crowdsourced Comprehension: Predicting Prerequisite Structure in Wikipedia
Partha Talukdar | William Cohen
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

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Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX)
James Fan | Raphael Hoffman | Aditya Kalyanpur | Sebastian Riedel | Fabian Suchanek | Partha Pratim Talukdar
Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX)

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Graph-based Semi-Supervised Learning Algorithms for NLP
Amar Subramanya | Partha Pratim Talukdar
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

2010

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Experiments in Graph-Based Semi-Supervised Learning Methods for Class-Instance Acquisition
Partha Pratim Talukdar | Fernando Pereira
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Learning Better Data Representation Using Inference-Driven Metric Learning
Paramveer S. Dhillon | Partha Pratim Talukdar | Koby Crammer
Proceedings of the ACL 2010 Conference Short Papers

2008

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Weakly-Supervised Acquisition of Labeled Class Instances using Graph Random Walks
Partha Pratim Talukdar | Joseph Reisinger | Marius Paşca | Deepak Ravichandran | Rahul Bhagat | Fernando Pereira
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2007

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Frustratingly Hard Domain Adaptation for Dependency Parsing
Mark Dredze | John Blitzer | Partha Pratim Talukdar | Kuzman Ganchev | João Graça | Fernando Pereira
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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Automatic Code Assignment to Medical Text
Koby Crammer | Mark Dredze | Kuzman Ganchev | Partha Pratim Talukdar | Steven Carroll
Biological, translational, and clinical language processing

2006

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A Context Pattern Induction Method for Named Entity Extraction
Partha Pratim Talukdar | Thorsten Brants | Mark Liberman | Fernando Pereira
Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X)

2004

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Automatic Generation of Compound Word Lexicon for Hindi Speech Synthesis
S.R. Deepa | Kalika Bali | A.G. Ramakrishnan | Partha Pratim Talukdar
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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