Mausam -

Also published as: Mausam, Mausam .


2023

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Do I have the Knowledge to Answer? Investigating Answerability of Knowledge Base Questions
Mayur Patidar | Prayushi Faldu | Avinash Singh | Lovekesh Vig | Indrajit Bhattacharya | Mausam -
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

When answering natural language questions over knowledge bases, missing facts, incomplete schema and limited scope naturally lead to many questions being unanswerable. While answerability has been explored in other QA settings, it has not been studied for QA over knowledge bases (KBQA). We create GrailQAbility, a new benchmark KBQA dataset with unanswerability, by first identifying various forms of KB incompleteness that make questions unanswerable, and then systematically adapting GrailQA (a popular KBQA dataset with only answerable questions). Experimenting with three state-of-the-art KBQA models, we find that all three models suffer a drop in performance even after suitable adaptation for unanswerable questions. In addition, these often detect unanswerability for wrong reasons and find specific forms of unanswerability particularly difficult to handle. This underscores the need for further research in making KBQA systems robust to unanswerability.

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DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles
Tanishq Gupta | Mohd Zaki | Devanshi Khatsuriya | Kausik Hira | N M Anoop Krishnan | Mausam -
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

A crucial component in the curation of KB for a scientific domain (e.g., materials science, food & nutrition, fuels) is information extraction from tables in the domain’s published research articles. To facilitate research in this direction, we define a novel NLP task of extracting compositions of materials (e.g., glasses) from tables in materials science papers. The task involves solving several challenges in concert, such as tables that mention compositions have highly varying structures; text in captions and full paper needs to be incorporated along with data in tables; and regular languages for numbers, chemical compounds, and composition expressions must be integrated into the model. We release a training dataset comprising 4,408 distantly supervised tables, along with 1,475 manually annotated dev and test tables. We also present DiSCoMaT, a strong baseline that combines multiple graph neural networks with several task-specific regular expressions, features, and constraints. We show that DiSCoMaT outperforms recent table processing architectures by significant margins. We release our code and data for further research on this challenging IE task from scientific tables.

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Multimodal Persona Based Generation of Comic Dialogs
Harsh Agrawal | Aditya Mishra | Manish Gupta | Mausam -
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We focus on the novel problem of persona based dialogue generation for comic strips. Dialogs in comic strips is a unique and unexplored area where every strip contains utterances from various characters with each one building upon the previous utterances and the associated visual scene. Previous works like DialoGPT, PersonaGPT and other dialog generation models encode two-party dialogues and do not account for the visual information. To the best of our knowledge we are the first to propose the paradigm of multimodal persona based dialogue generation. We contribute a novel dataset, ComSet, consisting of 54K strips, harvested from 13 popular comics available online. Further, we propose a multimodal persona-based architecture, MPDialog, to generate dialogues for the next panel in the strip which decreases the perplexity score by ~10 points over strong dialogue generation baseline models. We demonstrate that there is still ample opportunity for improvement, highlighting the importance of building stronger dialogue systems that are able to generate persona-consistent dialogues and understand the context through various modalities.

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mOKB6: A Multilingual Open Knowledge Base Completion Benchmark
Shubham Mittal | Keshav Kolluru | Soumen Chakrabarti | Mausam -
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Automated completion of open knowledge bases (Open KBs), which are constructed from triples of the form (subject phrase, relation phrase, object phrase), obtained via open information extraction (Open IE) system, are useful for discovering novel facts that may not be directly present in the text. However, research in Open KB completion (Open KBC) has so far been limited to resource-rich languages like English. Using the latest advances in multilingual Open IE, we construct the first multilingual Open KBC dataset, called mOKB6, containing facts from Wikipedia in six languages (including English). Improvingthe previous Open KB construction pipeline by doing multilingual coreference resolution andkeeping only entity-linked triples, we create a dense Open KB. We experiment with several models for the task and observe a consistent benefit of combining languages with the help of shared embedding space as well as translations of facts. We also observe that current multilingual models struggle to remember facts seen in languages of different scripts.

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Simple Augmentations of Logical Rules for Neuro-Symbolic Knowledge Graph Completion
Ananjan Nandi | Navdeep Kaur | Parag Singla | Mausam -
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

High-quality and high-coverage rule sets are imperative to the success of Neuro-Symbolic Knowledge Graph Completion (NS-KGC) models, because they form the basis of all symbolic inferences. Recent literature builds neural models for generating rule sets, however, preliminary experiments show that they struggle with maintaining high coverage. In this work, we suggest three simple augmentations to existing rule sets: (1) transforming rules to their abductive forms, (2) generating equivalent rules that use inverse forms of constituent relations and (3) random walks that propose new rules. Finally, we prune potentially low quality rules. Experiments over four datasets and five ruleset-baseline settings suggest that these simple augmentations consistently improve results, and obtain up to 7.1 pt MRR and 8.5 pt Hits@1 gains over using rules without augmentations.

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DKAF: KB Arbitration for Learning Task-Oriented Dialog Systems with Dialog-KB Inconsistencies
Vishal Saley | Rocktim Das | Dinesh Raghu | Mausam -
Findings of the Association for Computational Linguistics: ACL 2023

Task-oriented dialog (TOD) agents often ground their responses on external knowledge bases (KBs). These KBs can be dynamic and may be updated frequently. Existing approaches for learning TOD agents assume the KB snapshot contemporary to each individual dialog is available during training. However, in real-world scenarios, only the latest KB snapshot is available during training and as a result, the train dialogs may contain facts conflicting with the latest KB. These dialog-KB inconsistencies in the training data may potentially confuse the TOD agent learning algorithm.In this work, we define the novel problem of learning a TOD agent with dialog-KB inconsistencies in the training data. We propose a Dialog-KB Arbitration Framework (DKAF) which reduces the dialog-KB inconsistencies by predicting the contemporary KB snapshot for each train dialog. These predicted KB snapshots are then used for training downstream TOD agents. As there are no existing datasets with dialog-KB inconsistencies, we systematically introduce inconsistencies in two publicly available dialog datasets. We show that TOD agents trained with DKAF perform better than existing baselines on both these datasets.

2022

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Alignment-Augmented Consistent Translation for Multilingual Open Information Extraction
Keshav Kolluru | Muqeeth Mohammed | Shubham Mittal | Soumen Chakrabarti | Mausam .
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Progress with supervised Open Information Extraction (OpenIE) has been primarily limited to English due to the scarcity of training data in other languages. In this paper, we explore techniques to automatically convert English text for training OpenIE systems in other languages. We introduce the Alignment-Augmented Constrained Translation (AACTrans) model to translate English sentences and their corresponding extractions consistently with each other — with no changes to vocabulary or semantic meaning which may result from independent translations. Using the data generated with AACTrans, we train a novel two-stage generative OpenIE model, which we call Gen2OIE, that outputs for each sentence: 1) relations in the first stage and 2) all extractions containing the relation in the second stage. Gen2OIE increases relation coverage using a training data transformation technique that is generalizable to multiple languages, in contrast to existing models that use an English-specific training loss. Evaluations on 5 languages — Spanish, Portuguese, Chinese, Hindi and Telugu — show that the Gen2OIE with AACTrans data outperforms prior systems by a margin of 6-25% in F1.

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PARE: A Simple and Strong Baseline for Monolingual and Multilingual Distantly Supervised Relation Extraction
Vipul Rathore | Kartikeya Badola | Parag Singla | Mausam .
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Neural models for distantly supervised relation extraction (DS-RE) encode each sentence in an entity-pair bag separately. These are then aggregated for bag-level relation prediction. Since, at encoding time, these approaches do not allow information to flow from other sentences in the bag, we believe that they do not utilize the available bag data to the fullest. In response, we explore a simple baseline approach (PARE) in which all sentences of a bag are concatenated into a passage of sentences, and encoded jointly using BERT. The contextual embeddings of tokens are aggregated using attention with the candidate relation as query – this summary of whole passage predicts the candidate relation. We find that our simple baseline solution outperforms existing state-of-the-art DS-RE models in both monolingual and multilingual DS-RE datasets.

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DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction
Abhyuday Bhartiya | Kartikeya Badola | Mausam .
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Our goal is to study the novel task of distant supervision for multilingual relation extraction (Multi DS-RE). Research in Multi DS-RE has remained limited due to the absence of a reliable benchmarking dataset. The only available dataset for this task, RELX-Distant (Köksal and Özgür, 2020), displays several unrealistic characteristics, leading to a systematic overestimation of model performance. To alleviate these concerns, we release a new benchmark dataset for the task, named DiS-ReX. We also modify the widely-used bag attention models using an mBERT encoder and provide the first baseline results on the proposed task. We show that DiS-ReX serves as a more challenging dataset than RELX-Distant, leaving ample room for future research in this domain.

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“Covid vaccine is against Covid but Oxford vaccine is made at Oxford!” Semantic Interpretation of Proper Noun Compounds
Keshav Kolluru | Gabriel Stanovsky | Mausam -
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Proper noun compounds, e.g., “Covid vaccine”, convey information in a succinct manner (a “Covid vaccine” is a “vaccine that immunizes against the Covid disease”). These are commonly used in short-form domains, such as news headlines, but are largely ignored in information-seeking applications. To address this limitation, we release a new manually annotated dataset, ProNCI, consisting of 22.5K proper noun compounds along with their free-form semantic interpretations. ProNCI is 60 times larger than prior noun compound datasets and also includes non-compositional examples, which have not been previously explored. We experiment with various neural models for automatically generating the semantic interpretations from proper noun compounds, ranging from few-shot prompting to supervised learning, with varying degrees of knowledge about the constituent nouns. We find that adding targeted knowledge, particularly about the common noun, results in performance gains of upto 2.8%. Finally, we integrate our model generated interpretations with an existing Open IE system and observe an 7.5% increase in yield at a precision of 85%. The dataset and code are available at https://github.com/dair-iitd/pronci.

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Structural Constraints and Natural Language Inference for End-to-End Flowchart Grounded Dialog Response Generation
Dinesh Raghu | Suraj Joshi | Sachindra Joshi | Mausam -
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Flowchart grounded dialog systems converse with users by following a given flowchart and a corpus of FAQs. The existing state-of-the-art approach (Raghu et al, 2021) for learning such a dialog system, named FLONET, has two main limitations. (1) It uses a Retrieval Augmented Generation (RAG) framework which represents a flowchart as a bag of nodes. By doing so, it loses the connectivity structure between nodes that can aid in better response generation. (2) Typically dialogs progress with the agent asking polar (Y/N) questions, but users often respond indirectly without the explicit use of polar words. In such cases, it fails to understand the correct polarity of the answer. To overcome these issues, we propose Structure-Aware FLONET (SA-FLONET) which infuses structural constraints derived from the connectivity structure of flowcharts into the RAG framework. It uses natural language inference to better predict the polarity of indirect Y/N answers. We find that SA-FLONET outperforms FLONET, with a success rate improvement of 68% and 123% in flowchart grounded response generation and zero-shot flowchart grounded response generation tasks respectively.

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Joint Completion and Alignment of Multilingual Knowledge Graphs
Soumen Chakrabarti | Harkanwar Singh | Shubham Lohiya | Prachi Jain | Mausam -
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Knowledge Graph Completion (KGC) predicts missing facts in an incomplete Knowledge Graph (KG). Multilingual KGs associate entities and relations with surface forms written in different languages. An entity or relation may be associated with distinct IDs in different KGs, necessitating entity alignment (EA) and relation alignment (RA). Many effective algorithms have been proposed for completion and alignment as separate tasks. Here we show that these tasks are synergistic and best solved together. Our multitask approach starts with a state-of-the-art KG embedding scheme, but adds a novel relation representation based on sets of embeddings of (subject, object) entity pairs. This representation leads to a new relation alignment loss term based on a maximal bipartite matching between two sets of embedding vectors. This loss is combined with traditional KGC loss and optionally, losses based on text embeddings of entity (and relation) names. In experiments over KGs in seven languages, we find that our system achieves large improvements in KGC compared to a strong completion model that combines known facts in all languages. It also outperforms strong EA and RA baselines, underscoring the value of joint alignment and completion.

2021

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Constraint based Knowledge Base Distillation in End-to-End Task Oriented Dialogs
Dinesh Raghu | Atishya Jain | Mausam | Sachindra Joshi
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs
Dinesh Raghu | Shantanu Agarwal | Sachindra Joshi | Mausam
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

We propose a novel problem within end-to-end learning of task oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their problem (e.g., car not starting). Such dialogs are grounded in domain-specific flowcharts, which the agent is supposed to follow during the conversation. Our task exposes novel technical challenges for neural TOD, such as grounding an utterance to the flowchart without explicit annotation, referring to additional manual pages when user asks a clarification question, and ability to follow unseen flowcharts at test time. We release a dataset (FLODIAL) consisting of 2,738 dialogs grounded on 12 different troubleshooting flowcharts. We also design a neural model, FLONET, which uses a retrieval-augmented generation architecture to train the dialog agent. Our experiments find that FLONET can do zero-shot transfer to unseen flowcharts, and sets a strong baseline for future research.

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Unsupervised Learning of KB Queries in Task-Oriented Dialogs
Dinesh Raghu | Nikhil Gupta | Mausam
Transactions of the Association for Computational Linguistics, Volume 9

Task-oriented dialog (TOD) systems often need to formulate knowledge base (KB) queries corresponding to the user intent and use the query results to generate system responses. Existing approaches require dialog datasets to explicitly annotate these KB queries—these annotations can be time consuming, and expensive. In response, we define the novel problems of predicting the KB query and training the dialog agent, without explicit KB query annotation. For query prediction, we propose a reinforcement learning (RL) baseline, which rewards the generation of those queries whose KB results cover the entities mentioned in subsequent dialog. Further analysis reveals that correlation among query attributes in KB can significantly confuse memory augmented policy optimization (MAPO), an existing state of the art RL agent. To address this, we improve the MAPO baseline with simple but important modifications suited to our task. To train the full TOD system for our setting, we propose a pipelined approach: it independently predicts when to make a KB query (query position predictor), then predicts a KB query at the predicted position (query predictor), and uses the results of predicted query in subsequent dialog (next response predictor). Overall, our work proposes first solutions to our novel problem, and our analysis highlights the research challenges in training TOD systems without query annotation.

2020

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IMoJIE: Iterative Memory-Based Joint Open Information Extraction
Keshav Kolluru | Samarth Aggarwal | Vipul Rathore | Mausam | Soumen Chakrabarti
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

While traditional systems for Open Information Extraction were statistical and rule-based, recently neural models have been introduced for the task. Our work builds upon CopyAttention, a sequence generation OpenIE model (Cui et. al. 18). Our analysis reveals that CopyAttention produces a constant number of extractions per sentence, and its extracted tuples often express redundant information. We present IMoJIE, an extension to CopyAttention, which produces the next extraction conditioned on all previously extracted tuples. This approach overcomes both shortcomings of CopyAttention, resulting in a variable number of diverse extractions per sentence. We train IMoJIE on training data bootstrapped from extractions of several non-neural systems, which have been automatically filtered to reduce redundancy and noise. IMoJIE outperforms CopyAttention by about 18 F1 pts, and a BERT-based strong baseline by 2 F1 pts, establishing a new state of the art for the task.

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Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols
Prachi Jain | Sushant Rathi | Mausam | Soumen Chakrabarti
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Research on temporal knowledge bases, which associate a relational fact (s,r,o) with a validity time period (or time instant), is in its early days. Our work considers predicting missing entities (link prediction) and missing time intervals (time prediction) as joint Temporal Knowledge Base Completion (TKBC) tasks, and presents TIMEPLEX, a novel TKBC method, in which entities, relations and, time are all embedded in a uniform, compatible space. TIMEPLEX exploits the recurrent nature of some facts/events and temporal interactions between pairs of relations, yielding state-of-the-art results on both prediction tasks. We also find that existing TKBC models heavily overestimate link prediction performance due to imperfect evaluation mechanisms. In response, we propose improved TKBC evaluation protocols for both link and time prediction tasks, dealing with subtle issues that arise from the partial overlap of time intervals in gold instances and system predictions.

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OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction
Keshav Kolluru | Vaibhav Adlakha | Samarth Aggarwal | Mausam | Soumen Chakrabarti
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

A recent state-of-the-art neural open information extraction (OpenIE) system generates extractions iteratively, requiring repeated encoding of partial outputs. This comes at a significant computational cost. On the other hand,sequence labeling approaches for OpenIE are much faster, but worse in extraction quality. In this paper, we bridge this trade-off by presenting an iterative labeling-based system that establishes a new state of the art for OpenIE, while extracting 10x faster. This is achieved through a novel Iterative Grid Labeling (IGL) architecture, which treats OpenIE as a 2-D grid labeling task. We improve its performance further by applying coverage (soft) constraints on the grid at training time. Moreover, on observing that the best OpenIE systems falter at handling coordination structures, our OpenIE system also incorporates a new coordination analyzer built with the same IGL architecture. This IGL based coordination analyzer helps our OpenIE system handle complicated coordination structures, while also establishing a new state of the art on the task of coordination analysis, with a 12.3 pts improvement in F1 over previous analyzers. Our OpenIE system - OpenIE6 - beats the previous systems by as much as 4 pts in F1, while being much faster.

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A Simple Yet Strong Pipeline for HotpotQA
Dirk Groeneveld | Tushar Khot | Mausam | Ashish Sabharwal
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

State-of-the-art models for multi-hop question answering typically augment large-scale language models like BERT with additional, intuitively useful capabilities such as named entity recognition, graph-based reasoning, and question decomposition. However, does their strong performance on popular multi-hop datasets really justify this added design complexity? Our results suggest that the answer may be no, because even our simple pipeline based on BERT, named , performs surprisingly well. Specifically, on HotpotQA, Quark outperforms these models on both question answering and support identification (and achieves performance very close to a RoBERTa model). Our pipeline has three steps: 1) use BERT to identify potentially relevant sentences independently of each other; 2) feed the set of selected sentences as context into a standard BERT span prediction model to choose an answer; and 3) use the sentence selection model, now with the chosen answer, to produce supporting sentences. The strong performance of Quark resurfaces the importance of carefully exploring simple model designs before using popular benchmarks to justify the value of complex techniques.

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Why and when should you pool? Analyzing Pooling in Recurrent Architectures
Pratyush Maini | Keshav Kolluru | Danish Pruthi | Mausam
Findings of the Association for Computational Linguistics: EMNLP 2020

Pooling-based recurrent neural architectures consistently outperform their counterparts without pooling on sequence classification tasks. However, the reasons for their enhanced performance are largely unexamined. In this work, we examine three commonly used pooling techniques (mean-pooling, max-pooling, and attention, and propose *max-attention*, a novel variant that captures interactions among predictive tokens in a sentence. Using novel experiments, we demonstrate that pooling architectures substantially differ from their non-pooling equivalents in their learning ability and positional biases: (i) pooling facilitates better gradient flow than BiLSTMs in initial training epochs, and (ii) BiLSTMs are biased towards tokens at the beginning and end of the input, whereas pooling alleviates this bias. Consequently, we find that pooling yields large gains in low resource scenarios, and instances when salient words lie towards the middle of the input. Across several text classification tasks, we find max-attention to frequently outperform other pooling techniques.

2019

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Disentangling Language and Knowledge in Task-Oriented Dialogs
Dinesh Raghu | Nikhil Gupta | Mausam
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)

The Knowledge Base (KB) used for real-world applications, such as booking a movie or restaurant reservation, keeps changing over time. End-to-end neural networks trained for these task-oriented dialogs are expected to be immune to any changes in the KB. However, existing approaches breakdown when asked to handle such changes. We propose an encoder-decoder architecture (BoSsNet) with a novel Bag-of-Sequences (BoSs) memory, which facilitates the disentangled learning of the response’s language model and its knowledge incorporation. Consequently, the KB can be modified with new knowledge without a drop in interpretability. We find that BoSsNeT outperforms state-of-the-art models, with considerable improvements (>10%) on bAbI OOV test sets and other human-human datasets. We also systematically modify existing datasets to measure disentanglement and show BoSsNeT to be robust to KB modifications.

2018

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Type-Sensitive Knowledge Base Inference Without Explicit Type Supervision
Prachi Jain | Pankaj Kumar | Mausam | Soumen Chakrabarti
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

State-of-the-art knowledge base completion (KBC) models predict a score for every known or unknown fact via a latent factorization over entity and relation embeddings. We observe that when they fail, they often make entity predictions that are incompatible with the type required by the relation. In response, we enhance each base factorization with two type-compatibility terms between entity-relation pairs, and combine the signals in a novel manner. Without explicit supervision from a type catalog, our proposed modification obtains up to 7% MRR gains over base models, and new state-of-the-art results on several datasets. Further analysis reveals that our models better represent the latent types of entities and their embeddings also predict supervised types better than the embeddings fitted by baseline models.

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Open Information Extraction from Conjunctive Sentences
Swarnadeep Saha | Mausam
Proceedings of the 27th International Conference on Computational Linguistics

We develop CALM, a coordination analyzer that improves upon the conjuncts identified from dependency parses. It uses a language model based scoring and several linguistic constraints to search over hierarchical conjunct boundaries (for nested coordination). By splitting a conjunctive sentence around these conjuncts, CALM outputs several simple sentences. We demonstrate the value of our coordination analyzer in the end task of Open Information Extraction (Open IE). State-of-the-art Open IE systems lose substantial yield due to ineffective processing of conjunctive sentences. Our Open IE system, CALMIE, performs extraction over the simple sentences identified by CALM to obtain up to 1.8x yield with a moderate increase in precision compared to extractions from original sentences.

2017

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Bootstrapping for Numerical Open IE
Swarnadeep Saha | Harinder Pal | Mausam
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We design and release BONIE, the first open numerical relation extractor, for extracting Open IE tuples where one of the arguments is a number or a quantity-unit phrase. BONIE uses bootstrapping to learn the specific dependency patterns that express numerical relations in a sentence. BONIE’s novelty lies in task-specific customizations, such as inferring implicit relations, which are clear due to context such as units (for e.g., ‘square kilometers’ suggests area, even if the word ‘area’ is missing in the sentence). BONIE obtains 1.5x yield and 15 point precision gain on numerical facts over a state-of-the-art Open IE system.

2016

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Entity-balanced Gaussian pLSA for Automated Comparison
Danish Contractor | Parag Singla | Mausam
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Knowledge-Guided Linguistic Rewrites for Inference Rule Verification
Prachi Jain | Mausam
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Demonyms and Compound Relational Nouns in Nominal Open IE
Harinder Pal | Mausam
Proceedings of the 5th Workshop on Automated Knowledge Base Construction

2015

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Open IE as an Intermediate Structure for Semantic Tasks
Gabriel Stanovsky | Ido Dagan | Mausam
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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Hierarchical Summarization: Scaling Up Multi-Document Summarization
Janara Christensen | Stephen Soderland | Gagan Bansal | Mausam
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Modeling Missing Data in Distant Supervision for Information Extraction
Alan Ritter | Luke Zettlemoyer | Mausam | Oren Etzioni
Transactions of the Association for Computational Linguistics, Volume 1

Distant supervision algorithms learn information extraction models given only large readily available databases and text collections. Most previous work has used heuristics for generating labeled data, for example assuming that facts not contained in the database are not mentioned in the text, and facts in the database must be mentioned at least once. In this paper, we propose a new latent-variable approach that models missing data. This provides a natural way to incorporate side information, for instance modeling the intuition that text will often mention rare entities which are likely to be missing in the database. Despite the added complexity introduced by reasoning about missing data, we demonstrate that a carefully designed local search approach to inference is very accurate and scales to large datasets. Experiments demonstrate improved performance for binary and unary relation extraction when compared to learning with heuristic labels, including on average a 27% increase in area under the precision recall curve in the binary case.

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Generating Coherent Event Schemas at Scale
Niranjan Balasubramanian | Stephen Soderland | Mausam | Oren Etzioni
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Towards Coherent Multi-Document Summarization
Janara Christensen | Mausam | Stephen Soderland | Oren Etzioni
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

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Open Language Learning for Information Extraction
Mausam | Michael Schmitz | Stephen Soderland | Robert Bart | Oren Etzioni
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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No Noun Phrase Left Behind: Detecting and Typing Unlinkable Entities
Thomas Lin | Mausam | Oren Etzioni
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Entity Linking at Web Scale
Thomas Lin | Mausam | Oren Etzioni
Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX)

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Rel-grams: A Probabilistic Model of Relations in Text
Niranjan Balasubramanian | Stephen Soderland | Mausam | Oren Etzioni
Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX)

2011

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Named Entity Recognition in Tweets: An Experimental Study
Alan Ritter | Sam Clark | Mausam | Oren Etzioni
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2010

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Identifying Functional Relations in Web Text
Thomas Lin | Mausam | Oren Etzioni
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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A Latent Dirichlet Allocation Method for Selectional Preferences
Alan Ritter | Mausam | Oren Etzioni
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Semantic Role Labeling for Open Information Extraction
Janara Christensen | Mausam | Stephen Soderland | Oren Etzioni
Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading

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Machine Reading at the University of Washington
Hoifung Poon | Janara Christensen | Pedro Domingos | Oren Etzioni | Raphael Hoffmann | Chloe Kiddon | Thomas Lin | Xiao Ling | Mausam | Alan Ritter | Stefan Schoenmackers | Stephen Soderland | Dan Weld | Fei Wu | Congle Zhang
Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading

2009

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Compiling a Massive, Multilingual Dictionary via Probabilistic Inference
Mausam | Stephen Soderland | Oren Etzioni | Daniel Weld | Michael Skinner | Jeff Bilmes
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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A Rose is a Roos is a Ruusu: Querying Translations for Web Image Search
Janara Christensen | Mausam | Oren Etzioni
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers