Search Results for author: Junxian He

Found 22 papers, 17 papers with code

Prompt Consistency for Zero-Shot Task Generalization

1 code implementation29 Apr 2022 Chunting Zhou, Junxian He, Xuezhe Ma, Taylor Berg-Kirkpatrick, Graham Neubig

One of the most impressive results of recent NLP history is the ability of pre-trained language models to solve new tasks in a zero-shot setting.

Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval

1 code implementation28 Jan 2022 Uri Alon, Frank F. Xu, Junxian He, Sudipta Sengupta, Dan Roth, Graham Neubig

Retrieval-based language models (R-LM) model the probability of natural language text by combining a standard language model (LM) with examples retrieved from an external datastore at test time.

Language Modelling

Towards a Unified View of Parameter-Efficient Transfer Learning

1 code implementation ICLR 2022 Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, Graham Neubig

Furthermore, our unified framework enables the transfer of design elements across different approaches, and as a result we are able to instantiate new parameter-efficient fine-tuning methods that tune less parameters than previous methods while being more effective, achieving comparable results to fine-tuning all parameters on all four tasks.

Machine Translation Text Classification +2

Capturing Structural Locality in Non-parametric Language Models

no code implementations ICLR 2022 Frank F. Xu, Junxian He, Graham Neubig, Vincent J. Hellendoorn

Structural locality is a ubiquitous feature of real-world datasets, wherein data points are organized into local hierarchies.

Dependency Induction Through the Lens of Visual Perception

1 code implementation CoNLL (EMNLP) 2021 Ruisi Su, Shruti Rijhwani, Hao Zhu, Junxian He, Xinyu Wang, Yonatan Bisk, Graham Neubig

Our experiments find that concreteness is a strong indicator for learning dependency grammars, improving the direct attachment score (DAS) by over 50\% as compared to state-of-the-art models trained on pure text.

Constituency Grammar Induction Dependency Parsing

Efficient Nearest Neighbor Language Models

1 code implementation EMNLP 2021 Junxian He, Graham Neubig, Taylor Berg-Kirkpatrick

Non-parametric neural language models (NLMs) learn predictive distributions of text utilizing an external datastore, which allows them to learn through explicitly memorizing the training datapoints.

Domain Adaptation Language Modelling

CTRLsum: Towards Generic Controllable Text Summarization

1 code implementation8 Dec 2020 Junxian He, Wojciech Kryściński, Bryan McCann, Nazneen Rajani, Caiming Xiong

Our approach enables users to control multiple aspects of generated summaries by interacting with the summarization system through textual input in the form of a set of keywords or descriptive prompts.

Reading Comprehension Text Summarization

Learning Sparse Prototypes for Text Generation

1 code implementation NeurIPS 2020 Junxian He, Taylor Berg-Kirkpatrick, Graham Neubig

While effective, these methods are inefficient at test time as a result of needing to store and index the entire training corpus.

Language Modelling Prototype Selection +2

A Probabilistic Formulation of Unsupervised Text Style Transfer

5 code implementations ICLR 2020 Junxian He, Xinyi Wang, Graham Neubig, Taylor Berg-Kirkpatrick

Across all style transfer tasks, our approach yields substantial gains over state-of-the-art non-generative baselines, including the state-of-the-art unsupervised machine translation techniques that our approach generalizes.

Decipherment Language Modelling +6

Revisiting Self-Training for Neural Sequence Generation

1 code implementation ICLR 2020 Junxian He, Jiatao Gu, Jiajun Shen, Marc'Aurelio Ranzato

In this work, we first empirically show that self-training is able to decently improve the supervised baseline on neural sequence generation tasks.

Machine Translation Text Summarization +1

The Source-Target Domain Mismatch Problem in Machine Translation

no code implementations EACL 2021 Jiajun Shen, Peng-Jen Chen, Matt Le, Junxian He, Jiatao Gu, Myle Ott, Michael Auli, Marc'Aurelio Ranzato

While we live in an increasingly interconnected world, different places still exhibit strikingly different cultures and many events we experience in our every day life pertain only to the specific place we live in.

Machine Translation Translation

Choosing Transfer Languages for Cross-Lingual Learning

1 code implementation ACL 2019 Yu-Hsiang Lin, Chian-Yu Chen, Jean Lee, Zirui Li, Yuyan Zhang, Mengzhou Xia, Shruti Rijhwani, Junxian He, Zhisong Zhang, Xuezhe Ma, Antonios Anastasopoulos, Patrick Littell, Graham Neubig

Cross-lingual transfer, where a high-resource transfer language is used to improve the accuracy of a low-resource task language, is now an invaluable tool for improving performance of natural language processing (NLP) on low-resource languages.

Cross-Lingual Transfer Natural Language Processing

Lagging Inference Networks and Posterior Collapse in Variational Autoencoders

2 code implementations ICLR 2019 Junxian He, Daniel Spokoyny, Graham Neubig, Taylor Berg-Kirkpatrick

The variational autoencoder (VAE) is a popular combination of deep latent variable model and accompanying variational learning technique.

Text Generation

Unsupervised Learning of Syntactic Structure with Invertible Neural Projections

1 code implementation EMNLP 2018 Junxian He, Graham Neubig, Taylor Berg-Kirkpatrick

In this work, we propose a novel generative model that jointly learns discrete syntactic structure and continuous word representations in an unsupervised fashion by cascading an invertible neural network with a structured generative prior.

Constituency Grammar Induction POS +1

StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing

6 code implementations ACL 2018 Pengcheng Yin, Chunting Zhou, Junxian He, Graham Neubig

Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures.

Code Generation Semantic Parsing

Efficient Correlated Topic Modeling with Topic Embedding

no code implementations1 Jul 2017 Junxian He, Zhiting Hu, Taylor Berg-Kirkpatrick, Ying Huang, Eric P. Xing

Correlated topic modeling has been limited to small model and problem sizes due to their high computational cost and poor scaling.

Document Classification General Classification +1

Text Network Exploration via Heterogeneous Web of Topics

no code implementations2 Oct 2016 Junxian He, Ying Huang, Changfeng Liu, Jiaming Shen, Yuting Jia, Xinbing Wang

A text network refers to a data type that each vertex is associated with a text document and the relationship between documents is represented by edges.

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