Search Results for author: Lifeng Jin

Found 32 papers, 10 papers with code

Character-based PCFG Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages

no code implementations Findings (EMNLP) 2021 Lifeng Jin, Byung-Doh Oh, William Schuler

A subsequent evaluation on multilingual treebanks shows that the model with subword information achieves state-of-the-art results on many languages, further supporting a distributional model of syntactic acquisition.

Depth-Bounded Statistical PCFG Induction as a Model of Human Grammar Acquisition

no code implementations CL (ACL) 2021 Lifeng Jin, Lane Schwartz, Finale Doshi-Velez, Timothy Miller, William Schuler

Abstract This article describes a simple PCFG induction model with a fixed category domain that predicts a large majority of attested constituent boundaries, and predicts labels consistent with nearly half of attested constituent labels on a standard evaluation data set of child-directed speech.

Instance-adaptive training with noise-robust losses against noisy labels

no code implementations EMNLP 2021 Lifeng Jin, Linfeng Song, Kun Xu, Dong Yu

In order to alleviate the huge demand for annotated datasets for different tasks, many recent natural language processing datasets have adopted automated pipelines for fast-tracking usable data.

Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation

no code implementations14 Feb 2024 Xiaoying Zhang, Baolin Peng, Ye Tian, Jingyan Zhou, Lifeng Jin, Linfeng Song, Haitao Mi, Helen Meng

Despite showing increasingly human-like abilities, large language models (LLMs) often struggle with factual inaccuracies, i. e. "hallucinations", even when they hold relevant knowledge.

Inconsistent dialogue responses and how to recover from them

1 code implementation18 Jan 2024 Mian Zhang, Lifeng Jin, Linfeng Song, Haitao Mi, Dong Yu

One critical issue for chat systems is to stay consistent about preferences, opinions, beliefs and facts of itself, which has been shown a difficult problem.

TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs

1 code implementation9 Nov 2023 Shuyi Xie, Wenlin Yao, Yong Dai, Shaobo Wang, Donlin Zhou, Lifeng Jin, Xinhua Feng, Pengzhi Wei, Yujie Lin, Zhichao Hu, Dong Yu, Zhengyou Zhang, Jing Nie, Yuhong Liu

We construct a hierarchical task tree encompassing 7 major areas covering over 200 categories and over 800 tasks, which covers diverse capabilities such as question answering, reasoning, multiturn dialogue, and text generation, to evaluate LLMs in a comprehensive and in-depth manner.

Benchmarking Question Answering +1

Stabilizing RLHF through Advantage Model and Selective Rehearsal

no code implementations18 Sep 2023 Baolin Peng, Linfeng Song, Ye Tian, Lifeng Jin, Haitao Mi, Dong Yu

Large Language Models (LLMs) have revolutionized natural language processing, yet aligning these models with human values and preferences using RLHF remains a significant challenge.

Friend-training: Learning from Models of Different but Related Tasks

no code implementations31 Jan 2023 Mian Zhang, Lifeng Jin, Linfeng Song, Haitao Mi, Xiabing Zhou, Dong Yu

Current self-training methods such as standard self-training, co-training, tri-training, and others often focus on improving model performance on a single task, utilizing differences in input features, model architectures, and training processes.

Dialogue Rewriting Dialogue Understanding +1

Discover, Explanation, Improvement: An Automatic Slice Detection Framework for Natural Language Processing

no code implementations8 Nov 2022 Wenyue Hua, Lifeng Jin, Linfeng Song, Haitao Mi, Yongfeng Zhang, Dong Yu

Pretrained natural language processing (NLP) models have achieved high overall performance, but they still make systematic errors.

Learning a Grammar Inducer from Massive Uncurated Instructional Videos

1 code implementation22 Oct 2022 Songyang Zhang, Linfeng Song, Lifeng Jin, Haitao Mi, Kun Xu, Dong Yu, Jiebo Luo

While previous work focuses on building systems for inducing grammars on text that are well-aligned with video content, we investigate the scenario, in which text and video are only in loose correspondence.

Language Acquisition Video Alignment

Salience Allocation as Guidance for Abstractive Summarization

1 code implementation22 Oct 2022 Fei Wang, Kaiqiang Song, Hongming Zhang, Lifeng Jin, Sangwoo Cho, Wenlin Yao, Xiaoyang Wang, Muhao Chen, Dong Yu

Recent literature adds extractive summaries as guidance for abstractive summarization models to provide hints of salient content and achieves better performance.

Abstractive Text Summarization

Hierarchical Context Tagging for Utterance Rewriting

1 code implementation22 Jun 2022 Lisa Jin, Linfeng Song, Lifeng Jin, Dong Yu, Daniel Gildea

HCT (i) tags the source string with token-level edit actions and slotted rules and (ii) fills in the resulting rule slots with spans from the dialogue context.


Distant finetuning with discourse relations for stance classification

no code implementations27 Apr 2022 Lifeng Jin, Kun Xu, Linfeng Song, Dong Yu

Approaches for the stance classification task, an important task for understanding argumentation in debates and detecting fake news, have been relying on models which deal with individual debate topics.

Classification Stance Classification

Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories

2 code implementations EMNLP 2021 Wenlin Yao, Xiaoman Pan, Lifeng Jin, Jianshu Chen, Dian Yu, Dong Yu

We then train a model to identify semantic equivalence between a target word in context and one of its glosses using these aligned inventories, which exhibits strong transfer capability to many WSD tasks.

Sentence Word Sense Disambiguation

Domain-Adaptive Pretraining Methods for Dialogue Understanding

no code implementations ACL 2021 Han Wu, Kun Xu, Linfeng Song, Lifeng Jin, Haisong Zhang, Linqi Song

Language models like BERT and SpanBERT pretrained on open-domain data have obtained impressive gains on various NLP tasks.

Dialogue Understanding

Video-aided Unsupervised Grammar Induction

1 code implementation NAACL 2021 Songyang Zhang, Linfeng Song, Lifeng Jin, Kun Xu, Dong Yu, Jiebo Luo

We investigate video-aided grammar induction, which learns a constituency parser from both unlabeled text and its corresponding video.

Optical Character Recognition (OCR)

Grounded PCFG Induction with Images

no code implementations Asian Chapter of the Association for Computational Linguistics 2020 Lifeng Jin, William Schuler

Recent work in unsupervised parsing has tried to incorporate visual information into learning, but results suggest that these models need linguistic bias to compete against models that only rely on text.

Prepositional Phrase Attachment

Memory-bounded Neural Incremental Parsing for Psycholinguistic Prediction

no code implementations WS 2020 Lifeng Jin, William Schuler

Syntactic surprisal has been shown to have an effect on human sentence processing, and can be predicted from prefix probabilities of generative incremental parsers.


The Importance of Category Labels in Grammar Induction with Child-directed Utterances

no code implementations WS 2020 Lifeng Jin, William Schuler

Recent progress in grammar induction has shown that grammar induction is possible without explicit assumptions of language-specific knowledge.

Variance of Average Surprisal: A Better Predictor for Quality of Grammar from Unsupervised PCFG Induction

no code implementations ACL 2019 Lifeng Jin, William Schuler

In unsupervised grammar induction, data likelihood is known to be only weakly correlated with parsing accuracy, especially at convergence after multiple runs.

Model Selection

Unsupervised Learning of PCFGs with Normalizing Flow

no code implementations ACL 2019 Lifeng Jin, Finale Doshi-Velez, Timothy Miller, Lane Schwartz, William Schuler

This paper describes a neural PCFG inducer which employs context embeddings (Peters et al., 2018) in a normalizing flow model (Dinh et al., 2015) to extend PCFG induction to use semantic and morphological information.

Language Acquisition

Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction

1 code implementation EMNLP 2018 Lifeng Jin, Finale Doshi-Velez, Timothy Miller, William Schuler, Lane Schwartz

There have been several recent attempts to improve the accuracy of grammar induction systems by bounding the recursive complexity of the induction model (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016; Jin et al., 2018).

Unsupervised Grammar Induction with Depth-bounded PCFG

1 code implementation TACL 2018 Lifeng Jin, Finale Doshi-Velez, Timothy Miller, William Schuler, Lane Schwartz

There has been recent interest in applying cognitively or empirically motivated bounds on recursion depth to limit the search space of grammar induction models (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016).

Combining CNNs and Pattern Matching for Question Interpretation in a Virtual Patient Dialogue System

no code implementations WS 2017 Lifeng Jin, Michael White, Evan Jaffe, Laura Zimmerman, Douglas Danforth

For medical students, virtual patient dialogue systems can provide useful training opportunities without the cost of employing actors to portray standardized patients.


Memory-Bounded Left-Corner Unsupervised Grammar Induction on Child-Directed Input

no code implementations COLING 2016 Cory Shain, William Bryce, Lifeng Jin, Victoria Krakovna, Finale Doshi-Velez, Timothy Miller, William Schuler, Lane Schwartz

This paper presents a new memory-bounded left-corner parsing model for unsupervised raw-text syntax induction, using unsupervised hierarchical hidden Markov models (UHHMM).

Language Acquisition Sentence

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