Pengfei Liu

Design and Implementation of a Unified Mooc Recommendation System for Social Work Major: Experiences and Lessons

2017

MOOC is characterized by large amount of users and curriculums. The current main MOOC platforms are divided into information isolated island. So how to dynamic uniformly recommend courses to users that he or she is …

Learning Sparse Sharing Architectures for Multiple Tasks

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

Most existing deep multi-task learning models are based on parameter sharing, such as hard sharing, hierarchical sharing, and soft sharing. How choosing a suitable sharing mechanism depends on the relations among the tasks, which is …

RethinkCWS: Is Chinese Word Segmentation a Solved Task?

2020

The performance of the Chinese Word Segmentation (CWS) systems has gradually reached a plateau with the rapid development of deep neural networks, especially the successful use of large pre-trained models. In this paper, we take …

A teaching assistant system for big data analysis

2020 · Journal of Physics Conference Series

Abstract With the rapid development of big data analysis, cloud computing and mobile computing, the demand of teaching auxiliary processing and students’ online learning in colleges and universities depends more and more on information system. …

ExplainaBoard: An Explainable Leaderboard for NLP

2021 · arXiv (Cornell University)

With the rapid development of NLP research, leaderboards have emerged as one tool to track the performance of various systems on various NLP tasks. They are effective in this goal to some extent, but generally …

Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization

2023 · arXiv (Cornell University)

While large language models (LLMs) can already achieve strong performance on standard generic summarization benchmarks, their performance on more complex summarization task settings is less studied. Therefore, we benchmark LLMs on instruction controllable text summarization, …

Few-Shot Domain Adaption-Based Specific Emitter Identification Under Varying Modulation

2023

Specific emitter identification (SEI) is an effective Internet of things (IoT) data flow protection technique of identifying individual emitters via unique characteristics of different emitters. However, deep learning-based methods are difficult to deal with the …

Long Short-Term Memory Neural Networks for Chinese Word Segmentation

2015

Currently most of state-of-the-art methods for Chinese word segmentation are based on supervised learning, whose features are mostly extracted from a local context.These methods cannot utilize the long distance information which is also crucial for …

Multi-Timescale Long Short-Term Memory Neural Network for Modelling Sentences and Documents

2015

Neural network based methods have obtained great progress on a variety of natural language processing tasks. However, it is still a challenge task to model long texts, such as sentences and documents. In this paper, …

Recurrent Neural Network for Text Classification with Multi-Task Learning

2016 · arXiv (Cornell University)

Neural network based methods have obtained great progress on a variety of natural language processing tasks. However, in most previous works, the models are learned based on single-task supervised objectives, which often suffer from insufficient …

Adversarial Multi-task Learning for Text Classification

2017

Neural network models have shown their promising opportunities for multi-task learning, which focus on learning the shared layers to extract the common and task-invariant features. However, in most existing approaches, the extracted shared features are …

Searching for Effective Neural Extractive Summarization: What Works and What’s Next

2019

The recent years have seen remarkable success in the use of deep neural networks on text summarization. However, there is no clear understanding of why they perform so well, or how they might be improved. …

Heterogeneous Graph Neural Networks for Extractive Document Summarization

2020

As a crucial step in extractive document summarization, learning cross-sentence relations has been explored by a plethora of approaches. An intuitive way is to put them in the graphbased neural network, which has a more …

Extractive Summarization as Text Matching

2020

This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems. Instead of following the commonly used framework of extracting sentences individually and modeling the relationship between sentences, we …

Interpretable Multi-dataset Evaluation for Named Entity Recognition

2020

With the proliferation of models for natural language processing tasks, it is even harder to understand the differences between models and their relative merits. Simply looking at differences between holistic metrics such as accuracy, BLEU, …

SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization

2021

Yixin Liu, Pengfei Liu. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 2021.

BARTScore: Evaluating Generated Text as Text Generation

2021 · arXiv (Cornell University)

A wide variety of NLP applications, such as machine translation, summarization, and dialog, involve text generation. One major challenge for these applications is how to evaluate whether such generated texts are actually fluent, accurate, or …

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

2022 · ACM Computing Surveys

This article surveys and organizes research works in a new paradigm in natural language processing, which we dub “prompt-based learning.” Unlike traditional supervised learning, which trains a model to take in an input x and …

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

2021 · arXiv (Cornell University)

This paper surveys and organizes research works in a new paradigm in natural language processing, which we dub "prompt-based learning". Unlike traditional supervised learning, which trains a model to take in an input x and …