Search Results for author: Christopher D. Manning

Found 126 papers, 51 papers with code

GreaseLM: Graph REASoning Enhanced Language Models for Question Answering

1 code implementation21 Jan 2022 Xikun Zhang, Antoine Bosselut, Michihiro Yasunaga, Hongyu Ren, Percy Liang, Christopher D. Manning, Jure Leskovec

Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it.

Knowledge Graphs Pretrained Language Models +1

You Only Need One Model for Open-domain Question Answering

no code implementations14 Dec 2021 Haejun Lee, Akhil Kedia, Jongwon Lee, Ashwin Paranjape, Christopher D. Manning, Kyoung-Gu Woo

In this singular model architecture the hidden representations are progressively refined from the retriever to the reranker to the reader, which is more efficient use of model capacity and also leads to better gradient flow when we train it in an end-to-end manner.

Hard Attention Open-Domain Question Answering

Fast Model Editing at Scale

2 code implementations ICLR 2022 Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, Christopher D. Manning

To enable easy post-hoc editing at scale, we propose Model Editor Networks with Gradient Decomposition (MEND), a collection of small auxiliary editing networks that use a single desired input-output pair to make fast, local edits to a pre-trained model.

Language Modelling

Hindsight: Posterior-guided training of retrievers for improved open-ended generation

no code implementations ICLR 2022 Ashwin Paranjape, Omar Khattab, Christopher Potts, Matei Zaharia, Christopher D. Manning

Many text generation systems benefit from using a retriever to retrieve passages from a textual knowledge corpus (e. g., Wikipedia) which are then provided as additional context to the generator.

Text Generation

Conditional probing: measuring usable information beyond a baseline

1 code implementation EMNLP 2021 John Hewitt, Kawin Ethayarajh, Percy Liang, Christopher D. Manning

Probing experiments investigate the extent to which neural representations make properties -- like part-of-speech -- predictable.

Word Embeddings

On the Opportunities and Risks of Foundation Models

no code implementations16 Aug 2021 Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Kohd, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, aditi raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.

Transfer Learning

Neural Abstructions: Abstractions that Support Construction for Grounded Language Learning

no code implementations20 Jul 2021 Kaylee Burns, Christopher D. Manning, Li Fei-Fei

Although virtual agents are increasingly situated in environments where natural language is the most effective mode of interaction with humans, these exchanges are rarely used as an opportunity for learning.

Grounded language learning

Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering

1 code implementation ACL 2021 Siddharth Karamcheti, Ranjay Krishna, Li Fei-Fei, Christopher D. Manning

Active learning promises to alleviate the massive data needs of supervised machine learning: it has successfully improved sample efficiency by an order of magnitude on traditional tasks like topic classification and object recognition.

Active Learning Object Recognition +3

Capturing Logical Structure of Visually Structured Documents with Multimodal Transition Parser

no code implementations EMNLP (NLLP) 2021 Yuta Koreeda, Christopher D. Manning

While many NLP pipelines assume raw, clean texts, many texts we encounter in the wild, including a vast majority of legal documents, are not so clean, with many of them being visually structured documents (VSDs) such as PDFs.

Boundary Detection

Human-like informative conversations: Better acknowledgements using conditional mutual information

1 code implementation NAACL 2021 Ashwin Paranjape, Christopher D. Manning

This is because models trained with two contexts - new factual content and conversational history - generate responses that are non-specific w. r. t.

SLM: Learning a Discourse Language Representation with Sentence Unshuffling

no code implementations EMNLP 2020 Haejun Lee, Drew A. Hudson, Kangwook Lee, Christopher D. Manning

We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation in a fully self-supervised manner.

Language Modelling

The EOS Decision and Length Extrapolation

1 code implementation EMNLP (BlackboxNLP) 2020 Benjamin Newman, John Hewitt, Percy Liang, Christopher D. Manning

Extrapolation to unseen sequence lengths is a challenge for neural generative models of language.

Contrastive Learning of Medical Visual Representations from Paired Images and Text

4 code implementations2 Oct 2020 Yuhao Zhang, Hang Jiang, Yasuhide Miura, Christopher D. Manning, Curtis P. Langlotz

Learning visual representations of medical images is core to medical image understanding but its progress has been held back by the small size of hand-labeled datasets.

Contrastive Learning Image Classification

Neural Generation Meets Real People: Towards Emotionally Engaging Mixed-Initiative Conversations

no code implementations27 Aug 2020 Ashwin Paranjape, Abigail See, Kathleen Kenealy, Haojun Li, Amelia Hardy, Peng Qi, Kaushik Ram Sadagopan, Nguyet Minh Phu, Dilara Soylu, Christopher D. Manning

At the end of the competition, Chirpy Cardinal progressed to the finals with an average rating of 3. 6/5. 0, a median conversation duration of 2 minutes 16 seconds, and a 90th percentile duration of over 12 minutes.

Finding Universal Grammatical Relations in Multilingual BERT

1 code implementation ACL 2020 Ethan A. Chi, John Hewitt, Christopher D. Manning

Recent work has found evidence that Multilingual BERT (mBERT), a transformer-based multilingual masked language model, is capable of zero-shot cross-lingual transfer, suggesting that some aspects of its representations are shared cross-lingually.

Language Modelling Zero-Shot Cross-Lingual Transfer

Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection

no code implementations LREC 2020 Joakim Nivre, Marie-Catherine de Marneffe, Filip Ginter, Jan Hajič, Christopher D. Manning, Sampo Pyysalo, Sebastian Schuster, Francis Tyers, Daniel Zeman

Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework.

Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation

no code implementations ACL 2020 Kaustubh D. Dhole, Christopher D. Manning

Question Generation (QG) is fundamentally a simple syntactic transformation; however, many aspects of semantics influence what questions are good to form.

Question Generation Semantic Parsing +1

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

16 code implementations ICLR 2020 Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning

Then, instead of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that predicts whether each token in the corrupted input was replaced by a generator sample or not.

Language Modelling Masked Language Modeling +3

Answering Complex Open-domain Questions Through Iterative Query Generation

1 code implementation IJCNLP 2019 Peng Qi, Xiaowen Lin, Leo Mehr, Zijian Wang, Christopher D. Manning

It is challenging for current one-step retrieve-and-read question answering (QA) systems to answer questions like "Which novel by the author of 'Armada' will be adapted as a feature film by Steven Spielberg?"

Information Retrieval Pretrained Language Models +1

Learning by Abstraction: The Neural State Machine

3 code implementations NeurIPS 2019 Drew A. Hudson, Christopher D. Manning

We introduce the Neural State Machine, seeking to bridge the gap between the neural and symbolic views of AI and integrate their complementary strengths for the task of visual reasoning.

Visual Question Answering Visual Reasoning +1

What Does BERT Look At? An Analysis of BERT's Attention

2 code implementations WS 2019 Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D. Manning

Large pre-trained neural networks such as BERT have had great recent success in NLP, motivating a growing body of research investigating what aspects of language they are able to learn from unlabeled data.

Language Modelling

A Structural Probe for Finding Syntax in Word Representations

1 code implementation NAACL 2019 John Hewitt, Christopher D. Manning

Recent work has improved our ability to detect linguistic knowledge in word representations.

GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering

4 code implementations CVPR 2019 Drew A. Hudson, Christopher D. Manning

We introduce GQA, a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous VQA datasets.

Question Answering Visual Question Answering +2

Semi-Supervised Sequence Modeling with Cross-View Training

2 code implementations EMNLP 2018 Kevin Clark, Minh-Thang Luong, Christopher D. Manning, Quoc V. Le

We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data.

CCG Supertagging Dependency Parsing +6

Learning to Summarize Radiology Findings

1 code implementation WS 2018 Yuhao Zhang, Daisy Yi Ding, Tianpei Qian, Christopher D. Manning, Curtis P. Langlotz

The Impression section of a radiology report summarizes crucial radiology findings in natural language and plays a central role in communicating these findings to physicians.

Textual Analogy Parsing: What's Shared and What's Compared among Analogous Facts

2 code implementations EMNLP 2018 Matthew Lamm, Arun Tejasvi Chaganty, Christopher D. Manning, Dan Jurafsky, Percy Liang

To understand a sentence like "whereas only 10% of White Americans live at or below the poverty line, 28% of African Americans do" it is important not only to identify individual facts, e. g., poverty rates of distinct demographic groups, but also the higher-order relations between them, e. g., the disparity between them.

Frame Textual Analogy Parsing

Simpler but More Accurate Semantic Dependency Parsing

3 code implementations ACL 2018 Timothy Dozat, Christopher D. Manning

While syntactic dependency annotations concentrate on the surface or functional structure of a sentence, semantic dependency annotations aim to capture between-word relationships that are more closely related to the meaning of a sentence, using graph-structured representations.

Dependency Parsing Semantic Dependency Parsing

Sentences with Gapping: Parsing and Reconstructing Elided Predicates

2 code implementations NAACL 2018 Sebastian Schuster, Joakim Nivre, Christopher D. Manning

Sentences with gapping, such as Paul likes coffee and Mary tea, lack an overt predicate to indicate the relation between two or more arguments.

Natural Language Understanding Relation Extraction

Compositional Attention Networks for Machine Reasoning

9 code implementations ICLR 2018 Drew A. Hudson, Christopher D. Manning

We present the MAC network, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning.

Referring Expression Comprehension Visual Question Answering +1

Importance sampling for unbiased on-demand evaluation of knowledge base population

no code implementations EMNLP 2017 Arun Chaganty, Ashwin Paranjape, Percy Liang, Christopher D. Manning

Our first contribution is a new importance-sampling based evaluation which corrects for this bias by annotating a new system{'}s predictions on-demand via crowdsourcing.

Information Retrieval Knowledge Base Population +1

Stanford's Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task

no code implementations CONLL 2017 Timothy Dozat, Peng Qi, Christopher D. Manning

This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies.

Dependency Parsing

Arc-swift: A Novel Transition System for Dependency Parsing

1 code implementation ACL 2017 Peng Qi, Christopher D. Manning

Transition-based dependency parsers often need sequences of local shift and reduce operations to produce certain attachments.

Dependency Parsing

Get To The Point: Summarization with Pointer-Generator Networks

40 code implementations ACL 2017 Abigail See, Peter J. Liu, Christopher D. Manning

Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text).

Abstractive Text Summarization Document Summarization +1

SceneSeer: 3D Scene Design with Natural Language

no code implementations28 Feb 2017 Angel X. Chang, Mihail Eric, Manolis Savva, Christopher D. Manning

We present SceneSeer: an interactive text to 3D scene generation system that allows a user to design 3D scenes using natural language.

Scene Generation

A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue

no code implementations EACL 2017 Mihail Eric, Christopher D. Manning

Task-oriented dialogue focuses on conversational agents that participate in user-initiated dialogues on domain-specific topics.

Response Generation

Deep Biaffine Attention for Neural Dependency Parsing

23 code implementations6 Nov 2016 Timothy Dozat, Christopher D. Manning

This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser.

Dependency Parsing

Compression of Neural Machine Translation Models via Pruning

1 code implementation CONLL 2016 Abigail See, Minh-Thang Luong, Christopher D. Manning

Neural Machine Translation (NMT), like many other deep learning domains, typically suffers from over-parameterization, resulting in large storage sizes.

Machine Translation Translation

A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task

3 code implementations ACL 2016 Danqi Chen, Jason Bolton, Christopher D. Manning

Enabling a computer to understand a document so that it can answer comprehension questions is a central, yet unsolved goal of NLP.

Reading Comprehension

Learning Language Games through Interaction

3 code implementations ACL 2016 Sida I. Wang, Percy Liang, Christopher D. Manning

We introduce a new language learning setting relevant to building adaptive natural language interfaces.

Semantic Parsing

Improving Coreference Resolution by Learning Entity-Level Distributed Representations

1 code implementation ACL 2016 Kevin Clark, Christopher D. Manning

A long-standing challenge in coreference resolution has been the incorporation of entity-level information - features defined over clusters of mentions instead of mention pairs.

Coreference Resolution

A comparison of Named-Entity Disambiguation and Word Sense Disambiguation

no code implementations LREC 2016 Angel Chang, Valentin I. Spitkovsky, Christopher D. Manning, Eneko Agirre

Named Entity Disambiguation (NED) is the task of linking a named-entity mention to an instance in a knowledge-base, typically Wikipedia-derived resources like DBpedia.

Entity Disambiguation Word Sense Disambiguation

Evaluating the word-expert approach for Named-Entity Disambiguation

no code implementations15 Mar 2016 Angel X. Chang, Valentin I. Spitkovsky, Christopher D. Manning, Eneko Agirre

Named Entity Disambiguation (NED) is the task of linking a named-entity mention to an instance in a knowledge-base, typically Wikipedia.

Entity Disambiguation Word Sense Disambiguation

A large annotated corpus for learning natural language inference

1 code implementation EMNLP 2015 Samuel R. Bowman, Gabor Angeli, Christopher Potts, Christopher D. Manning

Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations.

Image Captioning Natural Language Inference

Effective Approaches to Attention-based Neural Machine Translation

47 code implementations EMNLP 2015 Minh-Thang Luong, Hieu Pham, Christopher D. Manning

Our ensemble model using different attention architectures has established a new state-of-the-art result in the WMT'15 English to German translation task with 25. 9 BLEU points, an improvement of 1. 0 BLEU points over the existing best system backed by NMT and an n-gram reranker.

 Ranked #1 on Machine Translation on 20NEWS (Accuracy metric)

Machine Translation Translation

Tree-structured composition in neural networks without tree-structured architectures

1 code implementation16 Jun 2015 Samuel R. Bowman, Christopher D. Manning, Christopher Potts

We hypothesize that neural sequence models like LSTMs are in fact able to discover and implicitly use recursive compositional structure, at least for tasks with clear cues to that structure in the data.

Text to 3D Scene Generation with Rich Lexical Grounding

no code implementations IJCNLP 2015 Angel Chang, Will Monroe, Manolis Savva, Christopher Potts, Christopher D. Manning

The ability to map descriptions of scenes to 3D geometric representations has many applications in areas such as art, education, and robotics.

Scene Generation

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

13 code implementations IJCNLP 2015 Kai Sheng Tai, Richard Socher, Christopher D. Manning

Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have obtained strong results on a variety of sequence modeling tasks.

General Classification Semantic Similarity +1

Learning Distributed Representations for Structured Output Prediction

no code implementations NeurIPS 2014 Vivek Srikumar, Christopher D. Manning

In recent years, distributed representations of inputs have led to performance gains in many applications by allowing statistical information to be shared across inputs.

Document Classification Part-Of-Speech Tagging +1

Global Belief Recursive Neural Networks

no code implementations NeurIPS 2014 Romain Paulus, Richard Socher, Christopher D. Manning

Recursive Neural Networks have recently obtained state of the art performance on several natural language processing tasks.

Sentiment Analysis

Simple MAP Inference via Low-Rank Relaxations

1 code implementation NeurIPS 2014 Roy Frostig, Sida Wang, Percy S. Liang, Christopher D. Manning

We focus on the problem of maximum a posteriori (MAP) inference in Markov random fields with binary variables and pairwise interactions.

Learning Distributed Word Representations for Natural Logic Reasoning

no code implementations15 Oct 2014 Samuel R. Bowman, Christopher Potts, Christopher D. Manning

Natural logic offers a powerful relational conception of meaning that is a natural counterpart to distributed semantic representations, which have proven valuable in a wide range of sophisticated language tasks.

Tensor Networks

Recursive Neural Networks Can Learn Logical Semantics

no code implementations WS 2015 Samuel R. Bowman, Christopher Potts, Christopher D. Manning

Tree-structured recursive neural networks (TreeRNNs) for sentence meaning have been successful for many applications, but it remains an open question whether the fixed-length representations that they learn can support tasks as demanding as logical deduction.

Relational Reasoning Tensor Networks

Universal Stanford dependencies: A cross-linguistic typology

no code implementations LREC 2014 Marie-Catherine de Marneffe, Timothy Dozat, Natalia Silveira, Katri Haverinen, Filip Ginter, Joakim Nivre, Christopher D. Manning

Revisiting the now de facto standard Stanford dependency representation, we propose an improved taxonomy to capture grammatical relations across languages, including morphologically rich ones.

Cross-lingual Projected Expectation Regularization for Weakly Supervised Learning

no code implementations TACL 2014 Mengqiu Wang, Christopher D. Manning

We consider a multilingual weakly supervised learning scenario where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide the learning in other languages.


Grounded Compositional Semantics for Finding and Describing Images with Sentences

no code implementations TACL 2014 Richard Socher, Andrej Karpathy, Quoc V. Le, Christopher D. Manning, Andrew Y. Ng

Previous work on Recursive Neural Networks (RNNs) shows that these models can produce compositional feature vectors for accurately representing and classifying sentences or images.

Relaxations for inference in restricted Boltzmann machines

no code implementations21 Dec 2013 Sida I. Wang, Roy Frostig, Percy Liang, Christopher D. Manning

We propose a relaxation-based approximate inference algorithm that samples near-MAP configurations of a binary pairwise Markov random field.

Reasoning With Neural Tensor Networks for Knowledge Base Completion

no code implementations NeurIPS 2013 Richard Socher, Danqi Chen, Christopher D. Manning, Andrew Ng

We assess the model by considering the problem of predicting additional true relations between entities given a partial knowledge base.

Knowledge Base Completion Tensor Networks

Cross-lingual Pseudo-Projected Expectation Regularization for Weakly Supervised Learning

no code implementations6 Oct 2013 Mengqiu Wang, Christopher D. Manning

We consider a multilingual weakly supervised learning scenario where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide the learning in other languages.


Robust Logistic Regression using Shift Parameters (Long Version)

no code implementations21 May 2013 Julie Tibshirani, Christopher D. Manning

Annotation errors can significantly hurt classifier performance, yet datasets are only growing noisier with the increased use of Amazon Mechanical Turk and techniques like distant supervision that automatically generate labels.

Named Entity Recognition

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