Search Results for author: Hannaneh Hajishirzi

Found 94 papers, 54 papers with code

Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text

1 code implementation EMNLP 2021 Christopher Clark, Jordi Salvador, Dustin Schwenk, Derrick Bonafilia, Mark Yatskar, Eric Kolve, Alvaro Herrasti, Jonghyun Choi, Sachin Mehta, Sam Skjonsberg, Carissa Schoenick, Aaron Sarnat, Hannaneh Hajishirzi, Aniruddha Kembhavi, Oren Etzioni, Ali Farhadi

Communicating with humans is challenging for AIs because it requires a shared understanding of the world, complex semantics (e. g., metaphors or analogies), and at times multi-modal gestures (e. g., pointing with a finger, or an arrow in a diagram).

MetaICL: Learning to Learn In Context

1 code implementation29 Oct 2021 Sewon Min, Mike Lewis, Luke Zettlemoyer, Hannaneh Hajishirzi

We introduce MetaICL (Meta-training for In-Context Learning), a new meta-training framework for few-shot learning where a pretrained language model is tuned to do in-context learn-ing on a large set of training tasks.

Few-Shot Learning Language Modelling +3

Generated Knowledge Prompting for Commonsense Reasoning

no code implementations15 Oct 2021 Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, Hannaneh Hajishirzi

Despite their ability to capture large amount of knowledge during pretraining, large-scale language models often benefit from incorporating external knowledge bases, especially on commonsense reasoning tasks.

Language Modelling

DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization

no code implementations EMNLP 2021 Zeqiu Wu, Bo-Ru Lu, Hannaneh Hajishirzi, Mari Ostendorf

Identifying relevant knowledge to be used in conversational systems that are grounded in long documents is critical to effective response generation.

Robust fine-tuning of zero-shot models

1 code implementation4 Sep 2021 Mitchell Wortsman, Gabriel Ilharco, Mike Li, Jong Wook Kim, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt

Compared to standard fine-tuning, the resulting weight-space ensembles provide large accuracy improvements out-of-distribution, while matching or improving in-distribution accuracy.

Fine-tuning

One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval

1 code implementation NeurIPS 2021 Akari Asai, Xinyan Yu, Jungo Kasai, Hannaneh Hajishirzi

We present Cross-lingual Open-Retrieval Answer Generation (CORA), the first unified many-to-many question answering (QA) model that can answer questions across many languages, even for ones without language-specific annotated data or knowledge sources.

Passage Retrieval Question Answering +1

FaVIQ: FAct Verification from Information-seeking Questions

1 code implementation5 Jul 2021 Jungsoo Park, Sewon Min, Jaewoo Kang, Luke Zettlemoyer, Hannaneh Hajishirzi

Our claims are verified to be natural, contain little lexical bias, and require a complete understanding of the evidence for verification.

Fact Checking Fact Verification +3

Prompting Contrastive Explanations for Commonsense Reasoning Tasks

no code implementations Findings (ACL) 2021 Bhargavi Paranjape, Julian Michael, Marjan Ghazvininejad, Luke Zettlemoyer, Hannaneh Hajishirzi

Many commonsense reasoning NLP tasks involve choosing between one or more possible answers to a question or prompt based on knowledge that is often implicit.

Efficient Passage Retrieval with Hashing for Open-domain Question Answering

1 code implementation ACL 2021 Ikuya Yamada, Akari Asai, Hannaneh Hajishirzi

Most state-of-the-art open-domain question answering systems use a neural retrieval model to encode passages into continuous vectors and extract them from a knowledge source.

Open-Domain Question Answering Passage Retrieval

Beyond Paragraphs: NLP for Long Sequences

1 code implementation NAACL 2021 Iz Beltagy, Arman Cohan, Hannaneh Hajishirzi, Sewon Min, Matthew E. Peters

In this tutorial, we aim at bringing interested NLP researchers up to speed about the recent and ongoing techniques for document-level representation learning.

Document-level Representation Learning

GooAQ: Open Question Answering with Diverse Answer Types

1 code implementation Findings (EMNLP) 2021 Daniel Khashabi, Amos Ng, Tushar Khot, Ashish Sabharwal, Hannaneh Hajishirzi, Chris Callison-Burch

GooAQ answers are mined from Google's responses to our collected questions, specifically from the answer boxes in the search results.

Question Answering

Cross-Task Generalization via Natural Language Crowdsourcing Instructions

1 code implementation18 Apr 2021 Swaroop Mishra, Daniel Khashabi, Chitta Baral, Hannaneh Hajishirzi

Humans (e. g., crowdworkers) have a remarkable ability in solving different tasks, by simply reading textual instructions that define them and looking at a few examples.

Question Answering

Joint Passage Ranking for Diverse Multi-Answer Retrieval

no code implementations EMNLP 2021 Sewon Min, Kenton Lee, Ming-Wei Chang, Kristina Toutanova, Hannaneh Hajishirzi

We study multi-answer retrieval, an under-explored problem that requires retrieving passages to cover multiple distinct answers for a given question.

Passage Retrieval Question Answering

NaturalProofs: Mathematical Theorem Proving in Natural Language

1 code implementation24 Mar 2021 Sean Welleck, Jiacheng Liu, Ronan Le Bras, Hannaneh Hajishirzi, Yejin Choi, Kyunghyun Cho

Understanding and creating mathematics using natural mathematical language - the mixture of symbolic and natural language used by humans - is a challenging and important problem for driving progress in machine learning.

Automated Theorem Proving Domain Generalization +1

IIRC: A Dataset of Incomplete Information Reading Comprehension Questions

no code implementations EMNLP 2020 James Ferguson, Matt Gardner, Hannaneh Hajishirzi, Tushar Khot, Pradeep Dasigi

However, most existing reading comprehension (RC) tasks only focus on questions for which the contexts provide all the information required to answer them, thus not evaluating a system's performance at identifying a potential lack of sufficient information and locating sources for that information.

Reading Comprehension

Extracting a Knowledge Base of Mechanisms from COVID-19 Papers

3 code implementations NAACL 2021 Tom Hope, Aida Amini, David Wadden, Madeleine van Zuylen, Sravanthi Parasa, Eric Horvitz, Daniel Weld, Roy Schwartz, Hannaneh Hajishirzi

The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge.

X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers

1 code implementation EMNLP 2020 Jaemin Cho, Jiasen Lu, Dustin Schwenk, Hannaneh Hajishirzi, Aniruddha Kembhavi

X-LXMERT's image generation capabilities rival state of the art generative models while its question answering and captioning abilities remains comparable to LXMERT.

Image Captioning Image Generation +3

Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics

4 code implementations EMNLP 2020 Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, Yejin Choi

Experiments across four datasets show that these model-dependent measures reveal three distinct regions in the data map, each with pronounced characteristics.

Extracting Summary Knowledge Graphs from Long Documents

1 code implementation19 Sep 2020 Zeqiu Wu, Rik Koncel-Kedziorski, Mari Ostendorf, Hannaneh Hajishirzi

Knowledge graphs capture entities and relations from long documents and can facilitate reasoning in many downstream applications.

Graph Learning Knowledge Graphs +1

DeLighT: Deep and Light-weight Transformer

1 code implementation ICLR 2021 Sachin Mehta, Marjan Ghazvininejad, Srinivasan Iyer, Luke Zettlemoyer, Hannaneh Hajishirzi

We introduce a deep and light-weight transformer, DeLighT, that delivers similar or better performance than standard transformer-based models with significantly fewer parameters.

Language Modelling Machine Translation +1

HATNet: An End-to-End Holistic Attention Network for Diagnosis of Breast Biopsy Images

1 code implementation25 Jul 2020 Sachin Mehta, Ximing Lu, Donald Weaver, Joann G. Elmore, Hannaneh Hajishirzi, Linda Shapiro

HATNet extends the bag-of-words approach and uses self-attention to encode global information, allowing it to learn representations from clinically relevant tissue structures without any explicit supervision.

Histopathological Image Classification Image Classification

Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web

no code implementations ACL 2020 Xin Luna Dong, Hannaneh Hajishirzi, Colin Lockard, Prashant Shiralkar

In this tutorial we take a holistic view toward information extraction, exploring the commonalities in the challenges and solutions developed to address these different forms of text.

Entity Linking

ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages

no code implementations ACL 2020 Colin Lockard, Prashant Shiralkar, Xin Luna Dong, Hannaneh Hajishirzi

In this work, we propose a solution for "zero-shot" open-domain relation extraction from webpages with a previously unseen template, including from websites with little overlap with existing sources of knowledge for distant supervision and websites in entirely new subject verticals.

Relation Extraction

UnifiedQA: Crossing Format Boundaries With a Single QA System

3 code implementations Findings of the Association for Computational Linguistics 2020 Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, Hannaneh Hajishirzi

As evidence, we use the latest advances in language modeling to build a single pre-trained QA model, UnifiedQA, that performs surprisingly well across 17 QA datasets spanning 4 diverse formats.

Common Sense Reasoning Fine-tuning +3

SciREX: A Challenge Dataset for Document-Level Information Extraction

1 code implementation ACL 2020 Sarthak Jain, Madeleine van Zuylen, Hannaneh Hajishirzi, Iz Beltagy

It is challenging to create a large-scale information extraction (IE) dataset at the document level since it requires an understanding of the whole document to annotate entities and their document-level relationships that usually span beyond sentences or even sections.

Document-level

A Controllable Model of Grounded Response Generation

1 code implementation1 May 2020 Zeqiu Wu, Michel Galley, Chris Brockett, Yizhe Zhang, Xiang Gao, Chris Quirk, Rik Koncel-Kedziorski, Jianfeng Gao, Hannaneh Hajishirzi, Mari Ostendorf, Bill Dolan

Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses.

Probing Contextual Language Models for Common Ground with Visual Representations

no code implementations NAACL 2021 Gabriel Ilharco, Rowan Zellers, Ali Farhadi, Hannaneh Hajishirzi

The success of large-scale contextual language models has attracted great interest in probing what is encoded in their representations.

Representation Learning

An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction

1 code implementation EMNLP 2020 Bhargavi Paranjape, Mandar Joshi, John Thickstun, Hannaneh Hajishirzi, Luke Zettlemoyer

Decisions of complex language understanding models can be rationalized by limiting their inputs to a relevant subsequence of the original text.

Language understanding

Fact or Fiction: Verifying Scientific Claims

1 code implementation EMNLP 2020 David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, Hannaneh Hajishirzi

We introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that SUPPORTS or REFUTES a given scientific claim, and to identify rationales justifying each decision.

Domain Adaptation Fact Checking

AmbigQA: Answering Ambiguous Open-domain Questions

1 code implementation EMNLP 2020 Sewon Min, Julian Michael, Hannaneh Hajishirzi, Luke Zettlemoyer

Ambiguity is inherent to open-domain question answering; especially when exploring new topics, it can be difficult to ask questions that have a single, unambiguous answer.

Open-Domain Question Answering

Procedural Reading Comprehension with Attribute-Aware Context Flow

no code implementations AKBC 2020 Aida Amini, Antoine Bosselut, Bhavana Dalvi Mishra, Yejin Choi, Hannaneh Hajishirzi

Procedural texts often describe processes (e. g., photosynthesis and cooking) that happen over entities (e. g., light, food).

Reading Comprehension

Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

3 code implementations15 Feb 2020 Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, Noah Smith

We publicly release all of our experimental data, including training and validation scores for 2, 100 trials, to encourage further analysis of training dynamics during fine-tuning.

Fine-tuning

Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering

2 code implementations ICLR 2020 Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, Richard Socher, Caiming Xiong

Answering questions that require multi-hop reasoning at web-scale necessitates retrieving multiple evidence documents, one of which often has little lexical or semantic relationship to the question.

Question Answering

Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering

5 code implementations10 Nov 2019 Sewon Min, Danqi Chen, Luke Zettlemoyer, Hannaneh Hajishirzi

We introduce an approach for open-domain question answering (QA) that retrieves and reads a passage graph, where vertices are passages of text and edges represent relationships that are derived from an external knowledge base or co-occurrence in the same article.

Open-Domain Question Answering Reading Comprehension +1

Contextualized Sparse Representations for Real-Time Open-Domain Question Answering

3 code implementations ACL 2020 Jinhyuk Lee, Minjoon Seo, Hannaneh Hajishirzi, Jaewoo Kang

Open-domain question answering can be formulated as a phrase retrieval problem, in which we can expect huge scalability and speed benefit but often suffer from low accuracy due to the limitation of existing phrase representation models.

Information Retrieval Open-Domain Question Answering

On Making Reading Comprehension More Comprehensive

no code implementations WS 2019 Matt Gardner, Jonathan Berant, Hannaneh Hajishirzi, Alon Talmor, Sewon Min

In this work, we justify a question answering approach to reading comprehension and describe the various kinds of questions one might use to more fully test a system{'}s comprehension of a passage, moving beyond questions that only probe local predicate-argument structures.

Machine Reading Comprehension Question Answering

Question Answering is a Format; When is it Useful?

no code implementations25 Sep 2019 Matt Gardner, Jonathan Berant, Hannaneh Hajishirzi, Alon Talmor, Sewon Min

In this opinion piece, we argue that question answering should be considered a format which is sometimes useful for studying particular phenomena, not a phenomenon or task in itself.

Machine Translation Question Answering +4

Entity, Relation, and Event Extraction with Contextualized Span Representations

2 code implementations IJCNLP 2019 David Wadden, Ulme Wennberg, Yi Luan, Hannaneh Hajishirzi

We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction.

Event Extraction Joint Entity and Relation Extraction +1

Mixture Content Selection for Diverse Sequence Generation

1 code implementation IJCNLP 2019 Jaemin Cho, Minjoon Seo, Hannaneh Hajishirzi

The diversification stage uses a mixture of experts to sample different binary masks on the source sequence for diverse content selection.

Abstractive Text Summarization Document Summarization +1

Potential-Based Advice for Stochastic Policy Learning

no code implementations20 Jul 2019 Baicen Xiao, Bhaskar Ramasubramanian, Andrew Clark, Hannaneh Hajishirzi, Linda Bushnell, Radha Poovendran

This paper augments the reward received by a reinforcement learning agent with potential functions in order to help the agent learn (possibly stochastic) optimal policies.

Q-Learning

Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index

1 code implementation ACL 2019 Minjoon Seo, Jinhyuk Lee, Tom Kwiatkowski, Ankur P. Parikh, Ali Farhadi, Hannaneh Hajishirzi

Existing open-domain question answering (QA) models are not suitable for real-time usage because they need to process several long documents on-demand for every input query.

Open-Domain Question Answering

DiCENet: Dimension-wise Convolutions for Efficient Networks

2 code implementations8 Jun 2019 Sachin Mehta, Hannaneh Hajishirzi, Mohammad Rastegari

When DiCE units are stacked to build the DiCENet model, we observe significant improvements over state-of-the-art models across various computer vision tasks including image classification, object detection, and semantic segmentation.

Image Classification Neural Architecture Search +2

MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms

no code implementations NAACL 2019 Aida Amini, Saadia Gabriel, Peter Lin, Rik Koncel-Kedziorski, Yejin Choi, Hannaneh Hajishirzi

We introduce a new representation language to model precise operation programs corresponding to each math problem that aim to improve both the performance and the interpretability of the learned models.

Math Word Problem Solving

A General Framework for Information Extraction using Dynamic Span Graphs

3 code implementations NAACL 2019 Yi Luan, Dave Wadden, Luheng He, Amy Shah, Mari Ostendorf, Hannaneh Hajishirzi

We introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs.

 Ranked #1 on Relation Extraction on ACE 2004 (Cross Sentence metric)

Joint Entity and Relation Extraction Named Entity Recognition

Text Generation from Knowledge Graphs with Graph Transformers

2 code implementations NAACL 2019 Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, Hannaneh Hajishirzi

Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce.

KG-to-Text Generation Knowledge Graphs +1

Pyramidal Recurrent Unit for Language Modeling

2 code implementations EMNLP 2018 Sachin Mehta, Rik Koncel-Kedziorski, Mohammad Rastegari, Hannaneh Hajishirzi

We introduce the Pyramidal Recurrent Unit (PRU), which enables learning representations in high dimensional space with more generalization power and fewer parameters.

Language Modelling

Scientific Relation Extraction with Selectively Incorporated Concept Embeddings

no code implementations26 Aug 2018 Yi Luan, Mari Ostendorf, Hannaneh Hajishirzi

This paper describes our submission for the SemEval 2018 Task 7 shared task on semantic relation extraction and classification in scientific papers.

Classification General Classification +1

Semi-Supervised Event Extraction with Paraphrase Clusters

no code implementations NAACL 2018 James Ferguson, Colin Lockard, Daniel S. Weld, Hannaneh Hajishirzi

Supervised event extraction systems are limited in their accuracy due to the lack of available training data.

Event Extraction

Data-Driven Methods for Solving Algebra Word Problems

no code implementations28 Apr 2018 Benjamin Robaidek, Rik Koncel-Kedziorski, Hannaneh Hajishirzi

We explore contemporary, data-driven techniques for solving math word problems over recent large-scale datasets.

Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension

1 code implementation EMNLP 2018 Minjoon Seo, Tom Kwiatkowski, Ankur P. Parikh, Ali Farhadi, Hannaneh Hajishirzi

We formalize a new modular variant of current question answering tasks by enforcing complete independence of the document encoder from the question encoder.

Question Answering Reading Comprehension

Identifying Most Walkable Direction for Navigation in an Outdoor Environment

no code implementations21 Nov 2017 Sachin Mehta, Hannaneh Hajishirzi, Linda Shapiro

We present an approach for identifying the most walkable direction for navigation using a hand-held camera.

Semantic Segmentation

Neural Speed Reading via Skim-RNN

1 code implementation ICLR 2018 Minjoon Seo, Sewon Min, Ali Farhadi, Hannaneh Hajishirzi

Inspired by the principles of speed reading, we introduce Skim-RNN, a recurrent neural network (RNN) that dynamically decides to update only a small fraction of the hidden state for relatively unimportant input tokens.

Scientific Information Extraction with Semi-supervised Neural Tagging

no code implementations EMNLP 2017 Yi Luan, Mari Ostendorf, Hannaneh Hajishirzi

This paper addresses the problem of extracting keyphrases from scientific articles and categorizing them as corresponding to a task, process, or material.

Named Entity Recognition

Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension

no code implementations CVPR 2017 Aniruddha Kembhavi, Minjoon Seo, Dustin Schwenk, Jonghyun Choi, Ali Farhadi, Hannaneh Hajishirzi

Our analysis shows that a significant portion of questions require complex parsing of the text and the diagrams and reasoning, indicating that our dataset is more complex compared to previous machine comprehension and visual question answering datasets.

Question Answering Reading Comprehension +1

Question Answering through Transfer Learning from Large Fine-grained Supervision Data

1 code implementation ACL 2017 Sewon Min, Minjoon Seo, Hannaneh Hajishirzi

We show that the task of question answering (QA) can significantly benefit from the transfer learning of models trained on a different large, fine-grained QA dataset.

Question Answering Transfer Learning

Bidirectional Attention Flow for Machine Comprehension

23 code implementations5 Nov 2016 Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, Hannaneh Hajishirzi

Machine comprehension (MC), answering a query about a given context paragraph, requires modeling complex interactions between the context and the query.

Cloze Test Open-Domain Question Answering +1

A Theme-Rewriting Approach for Generating Algebra Word Problems

no code implementations EMNLP 2016 Rik Koncel-Kedziorski, Ioannis Konstas, Luke Zettlemoyer, Hannaneh Hajishirzi

Texts present coherent stories that have a particular theme or overall setting, for example science fiction or western.

Text Generation

Query-Reduction Networks for Question Answering

2 code implementations14 Jun 2016 Minjoon Seo, Sewon Min, Ali Farhadi, Hannaneh Hajishirzi

In this paper, we study the problem of question answering when reasoning over multiple facts is required.

Goal-Oriented Dialog Question Answering

A Task-Oriented Approach for Cost-Sensitive Recognition

no code implementations CVPR 2016 Roozbeh Mottaghi, Hannaneh Hajishirzi, Ali Farhadi

With the recent progress in visual recognition, we have already started to see a surge of vision related real-world applications.

Scene Understanding

Disfluency Detection using a Bidirectional LSTM

no code implementations12 Apr 2016 Vicky Zayats, Mari Ostendorf, Hannaneh Hajishirzi

We introduce a new approach for disfluency detection using a Bidirectional Long-Short Term Memory neural network (BLSTM).

A Diagram Is Worth A Dozen Images

1 code implementation24 Mar 2016 Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, Ali Farhadi

We define syntactic parsing of diagrams as learning to infer DPGs for diagrams and study semantic interpretation and reasoning of diagrams in the context of diagram question answering.

Visual Question Answering

Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects

no code implementations2 Feb 2016 Hessam Bagherinezhad, Hannaneh Hajishirzi, Yejin Choi, Ali Farhadi

In this paper, we introduce a method to automatically infer object sizes, leveraging visual and textual information from web.

Visual Reasoning

Talking to the crowd: What do people react to in online discussions?

no code implementations EMNLP 2015 Aaron Jaech, Victoria Zayats, Hao Fang, Mari Ostendorf, Hannaneh Hajishirzi

This paper addresses the question of how language use affects community reaction to comments in online discussion forums, and the relative importance of the message vs. the messenger.

Discriminative and Consistent Similarities in Instance-Level Multiple Instance Learning

no code implementations CVPR 2015 Mohammad Rastegari, Hannaneh Hajishirzi, Ali Farhadi

In this paper we present a bottom-up method to instance-level Multiple Instance Learning (MIL) that learns to discover positive instances with globally constrained reasoning about local pairwise similarities.

Multiple Instance Learning Text Categorization

Parsing Algebraic Word Problems into Equations

no code implementations TACL 2015 Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ashish Sabharwal, Oren Etzioni, Siena Dumas Ang

This paper formalizes the problem of solving multi-sentence algebraic word problems as that of generating and scoring equation trees.

Coreference Resolution

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