106 citations · 231 across the 24 of their papers we have counts for
24 papers
Logic Agent: Enhancing Validity with Logic Rule Invocation
Hanmeng Liu, Zhiyang Teng, Chaoli Zhang +1
Chain-of-Thought (CoT) prompting has emerged as a pivotal technique for augmenting the inferential capabilities of language models during reasoning tasks. Despite its advancements,…
Multi-scale 2D Temporal Map Diffusion Models for Natural Language Video Localization
Chongzhi Zhang, Mingyuan Zhang, Zhiyang Teng +5
Natural Language Video Localization (NLVL), grounding phrases from natural language descriptions to corresponding video segments, is a complex yet critical task in video understand…
How Well Do Text Embedding Models Understand Syntax?
Yan Zhang, Zhaopeng Feng, Zhiyang Teng +2
Text embedding models have significantly contributed to advancements in natural language processing by adeptly capturing semantic properties of textual data. However, the ability o…
GLoRE: Evaluating Logical Reasoning of Large Language Models
Hanmeng liu, Zhiyang Teng, Ruoxi Ning +4
Large language models (LLMs) have shown significant general language understanding abilities. However, there has been a scarcity of attempts to assess the logical reasoning capacit…
Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature
Guangsheng Bao, Yanbin Zhao, Zhiyang Teng +2
Large language models (LLMs) have shown the ability to produce fluent and cogent content, presenting both productivity opportunities and societal risks. To build trustworthy AI sys…
Token-Level Fitting Issues of Seq2seq Models
Guangsheng Bao, Zhiyang Teng, Yue Zhang
Sequence-to-sequence (seq2seq) models have been widely used for natural language processing, computer vision, and other deep learning tasks. We find that seq2seq models trained wit…
Target-Side Augmentation for Document-Level Machine Translation
Guangsheng Bao, Zhiyang Teng, Yue Zhang
Document-level machine translation faces the challenge of data sparsity due to its long input length and a small amount of training data, increasing the risk of learning spurious p…
Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis
Xuming Hu, Zhijiang Guo, Zhiyang Teng +2
Multimodal relation extraction (MRE) is the task of identifying the semantic relationships between two entities based on the context of the sentence image pair. Existing retrieval-…
Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4
Hanmeng Liu, Ruoxi Ning, Zhiyang Teng +3
Harnessing logical reasoning ability is a comprehensive natural language understanding endeavor. With the release of Generative Pretrained Transformer 4 (GPT-4), highlighted as "ad…
Non-Autoregressive Document-Level Machine Translation
Guangsheng Bao, Zhiyang Teng, Hao Zhou +2
Non-autoregressive translation (NAT) models achieve comparable performance and superior speed compared to auto-regressive translation (AT) models in the context of sentence-level m…
LogiCoT: Logical Chain-of-Thought Instruction-Tuning
Hanmeng Liu, Zhiyang Teng, Leyang Cui +3
Generative Pre-trained Transformer 4 (GPT-4) demonstrates impressive chain-of-thought reasoning ability. Recent work on self-instruction tuning, such as Alpaca, has focused on enha…
Pre-Training a Graph Recurrent Network for Language Representation
Yile Wang, Linyi Yang, Zhiyang Teng +2
Transformer-based pre-trained models have gained much advance in recent years, becoming one of the most important backbones in natural language processing. Recent work shows that t…
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets
Peilin Zhou, Zeqiang Wang, Dading Chong +6
The COVID-19 pandemic continues to bring up various topics discussed or debated on social media. In order to explore the impact of pandemics on people's lives, it is crucial to und…
YATO: Yet Another deep learning based Text analysis Open toolkit
Zeqiang Wang, Yile Wang, Jiageng Wu +2
We introduce YATO, an open-source, easy-to-use toolkit for text analysis with deep learning. Different from existing heavily engineered toolkits and platforms, YATO is lightweight…
Solving Aspect Category Sentiment Analysis as a Text Generation Task
Jian Liu, Zhiyang Teng, Leyang Cui +2
Aspect category sentiment analysis has attracted increasing research attention. The dominant methods make use of pre-trained language models by learning effective aspect category-s…
G-Transformer for Document-level Machine Translation
Guangsheng Bao, Yue Zhang, Zhiyang Teng +2
Document-level MT models are still far from satisfactory. Existing work extend translation unit from single sentence to multiple sentences. However, study shows that when we furthe…
End-to-End Chinese Parsing Exploiting Lexicons
Yuan Zhang, Zhiyang Teng, Yue Zhang
Chinese parsing has traditionally been solved by three pipeline systems including word-segmentation, part-of-speech tagging and dependency parsing modules. In this paper, we propos…
SemGloVe: Semantic Co-occurrences for GloVe from BERT
Leilei Gan, Zhiyang Teng, Yue Zhang +3
GloVe learns word embeddings by leveraging statistical information from word co-occurrence matrices. However, word pairs in the matrices are extracted from a predefined local conte…
Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation
Yan Zhang, Zhijiang Guo, Zhiyang Teng +4
AMR-to-text generation is used to transduce Abstract Meaning Representation structures (AMR) into text. A key challenge in this task is to efficiently learn effective graph represe…
Dialogue State Induction Using Neural Latent Variable Models
Qingkai Min, Libo Qin, Zhiyang Teng +2
Dialogue state modules are a useful component in a task-oriented dialogue system. Traditional methods find dialogue states by manually labeling training corpora, upon which neural…
Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning
Zhijiang Guo, Yan Zhang, Zhiyang Teng +1
We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation. To capture structural information associated with grap…
Two Local Models for Neural Constituent Parsing
Zhiyang Teng, Yue Zhang
Non-local features have been exploited by syntactic parsers for capturing dependencies between sub output structures. Such features have been a key to the success of state-of-the-a…
Combining Discrete and Neural Features for Sequence Labeling
Jie Yang, Zhiyang Teng, Meishan Zhang +1
Neural network models have recently received heated research attention in the natural language processing community. Compared with traditional models with discrete features, neural…
Bidirectional Tree-Structured LSTM with Head Lexicalization
Zhiyang Teng, Yue Zhang
Sequential LSTM has been extended to model tree structures, giving competitive results for a number of tasks. Existing methods model constituent trees by bottom-up combinations of…