Search Results for author:
Found 107 papers, 38 papers with code
Entity Relative Position Representation based Multi-head Selection for Joint Entity and Relation Extraction
Joint entity and relation extraction has received increasing interests recently, due to the capability of utilizing the interactions between both steps.
TWT: Table with Written Text for Controlled Data-to-Text Generation
In this paper, we propose to generate text conditioned on the structured data (table) and a prefix (the written text) by leveraging the pre-trained models.
IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web
We have conducted extensive experiments on existing large multimodal models, offering insights into their performance and areas for improvement in image-to-web domain.
FuzzCoder: Byte-level Fuzzing Test via Large Language Model
In this work, we propose to adopt fine-tuned large language models (FuzzCoder) to learn patterns in the input files from successful attacks to guide future fuzzing explorations.
TableBench: A Comprehensive and Complex Benchmark for Table Question Answering
Recent advancements in Large Language Models (LLMs) have markedly enhanced the interpretation and processing of tabular data, introducing previously unimaginable capabilities.
RB-SQL: A Retrieval-based LLM Framework for Text-to-SQL
Large language models (LLMs) with in-context learning have significantly improved the performance of text-to-SQL task.
Raw Text is All you Need: Knowledge-intensive Multi-turn Instruction Tuning for Large Language Model
In this paper, we present a novel framework named R2S that leverages the CoD-Chain of Dialogue logic to guide large language models (LLMs) in generating knowledge-intensive multi-turn dialogues for instruction tuning.
UniCoder: Scaling Code Large Language Model via Universal Code
When applying LLMs for code generation, recent works mainly focus on directing the models to articulate intermediate natural-language reasoning steps, as in chain-of-thought (CoT) prompting, and then output code with the natural language or other structured intermediate steps.
Towards Real-world Scenario: Imbalanced New Intent Discovery
New Intent Discovery (NID) aims at detecting known and previously undefined categories of user intent by utilizing limited labeled and massive unlabeled data.
EMERGE: Integrating RAG for Improved Multimodal EHR Predictive Modeling
However, current models that utilize clinical notes and multivariate time-series EHR data often lack the necessary medical context for precise clinical tasks.
XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser
In the domain of document AI, semi-structured form parsing plays a crucial role.
ECLIPSE: Semantic Entropy-LCS for Cross-Lingual Industrial Log Parsing
These challenges are two-fold: 1) massive log templates: The performance and efficiency of most existing parsers will be significantly reduced when logs of growing quantities and different lengths; 2) Complex and changeable semantics: Traditional template-matching algorithms cannot accurately match the log templates of complicated industrial logs because they cannot utilize cross-language logs with similar semantics.
Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering
Multi-choice Machine Reading Comprehension (MMRC) aims to select the correct answer from a set of options based on a given passage and question.
RoNID: New Intent Discovery with Generated-Reliable Labels and Cluster-friendly Representations
To address the aforementioned challenges, we propose a Robust New Intent Discovery (RoNID) framework optimized by an EM-style method, which focuses on constructing reliable pseudo-labels and obtaining cluster-friendly discriminative representations.
m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt
Our method aims to minimize the representation distance of different languages by regarding the image as a central language.
New Intent Discovery with Attracting and Dispersing Prototype
New Intent Discovery (NID) aims to recognize known and infer new intent categories with the help of limited labeled and large-scale unlabeled data.
ResLoRA: Identity Residual Mapping in Low-Rank Adaption
As one of the most popular parameter-efficient fine-tuning (PEFT) methods, low-rank adaptation (LoRA) is commonly applied to fine-tune large language models (LLMs).
Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging
Log parsing, which entails transforming raw log messages into structured templates, constitutes a critical phase in the automation of log analytics.
C-ICL: Contrastive In-context Learning for Information Extraction
There has been increasing interest in exploring the capabilities of advanced large language models (LLMs) in the field of information extraction (IE), specifically focusing on tasks related to named entity recognition (NER) and relation extraction (RE).
REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models
Leveraging clinical notes and multivariate time-series EHR, existing models often lack the medical context relevent to clinical tasks, prompting the incorporation of external knowledge, particularly from the knowledge graph (KG).
SVIPTR: Fast and Efficient Scene Text Recognition with Vision Permutable Extractor
In this work, we propose a VIsion Permutable extractor for fast and efficient Scene Text Recognition (SVIPTR), which achieves an impressive balance between high performance and rapid inference speeds in the domain of STR.
MLAD: A Unified Model for Multi-system Log Anomaly Detection
In spite of the rapid advancements in unsupervised log anomaly detection techniques, the current mainstream models still necessitate specific training for individual system datasets, resulting in costly procedures and limited scalability due to dataset size, thereby leading to performance bottlenecks.
xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning
To bridge the gap among different languages, we propose a cross-lingual instruction fine-tuning framework (xCOT) to transfer knowledge from high-resource languages to low-resource languages.
LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly Detection
Log anomaly detection is a key component in the field of artificial intelligence for IT operations (AIOps).
MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL
Our framework comprises a core decomposer agent for Text-to-SQL generation with few-shot chain-of-thought reasoning, accompanied by two auxiliary agents that utilize external tools or models to acquire smaller sub-databases and refine erroneous SQL queries.
M2C: Towards Automatic Multimodal Manga Complement
In other words, the Multimodal Manga Complement (M2C) task has not been investigated, which aims to handle the aforementioned issues by providing a shared semantic space for vision and language understanding.
Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization
Specifically, we first conduct domain-aware pre-training using large-scale multi-scenario multi-domain dialogue data to enhance the adaptability of our pre-trained model.
OWL: A Large Language Model for IT Operations
However, there is a lack of specialized LLMs for IT operations.
Unleashing Potential of Evidence in Knowledge-Intensive Dialogue Generation
To fully Unleash the potential of evidence, we propose a framework to effectively incorporate Evidence in knowledge-Intensive Dialogue Generation (u-EIDG).
mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view Contrastive Learning
A multi-view contrastive learning framework is introduced to encompass semantic contrasts between source, codeswitched, and target sentences, as well as contrasts among token-to-token relations.
MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction
Cross-lingual open information extraction aims to extract structured information from raw text across multiple languages.
KnowPrefix-Tuning: A Two-Stage Prefix-Tuning Framework for Knowledge-Grounded Dialogue Generation
We propose Knowledgeable Prefix Tuning (KnowPrefix-Tuning), a two-stage tuning framework, bypassing the retrieval process in a knowledge-grounded conversation system by injecting prior knowledge into the lightweight knowledge prefix.
GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking
However, the retrieved passages are not ideal for guiding answer generation because of the discrepancy between retrieval and generation, i. e., the candidate passages are all treated equally during the retrieval procedure without considering their potential to generate a proper answer.
QURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic Parsing
Context-dependent Text-to-SQL aims to translate multi-turn natural language questions into SQL queries.
Enhancing Large Language Model with Self-Controlled Memory Framework
Large Language Models (LLMs) are constrained by their inability to process lengthy inputs, resulting in the loss of critical historical information.
API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs
(2) How can we enhance LLMs' ability to utilize tools?
Retrieval-Augmented Classification with Decoupled Representation
Retrieval augmented methods have shown promising results in various classification tasks.
Character, Word, or Both? Revisiting the Segmentation Granularity for Chinese Pre-trained Language Models
We conduct extensive experiments on various Chinese NLP tasks to evaluate existing PLMs as well as the proposed MigBERT.
Multi-task Transformer with Relation-attention and Type-attention for Named Entity Recognition
There are three types of NER tasks, including flat, nested and discontinuous entity recognition.
Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality
The global one aims to identify vital sub-topics in the dialogue and the local one aims to select the most important context in each sub-topic.
HanoiT: Enhancing Context-aware Translation via Selective Context
Context-aware neural machine translation aims to use the document-level context to improve translation quality.
Multilingual Entity and Relation Extraction from Unified to Language-specific Training
In this paper, we propose a two-stage multilingual training method and a joint model called Multilingual Entity and Relation Extraction framework (mERE) to mitigate language interference across languages.
GanLM: Encoder-Decoder Pre-training with an Auxiliary Discriminator
Inspired by the idea of Generative Adversarial Networks (GANs), we propose a GAN-style model for encoder-decoder pre-training by introducing an auxiliary discriminator, unifying the ability of language understanding and generation in a single model.
Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation
The robustness of Text-to-SQL parsers against adversarial perturbations plays a crucial role in delivering highly reliable applications.
Know What I don't Know: Handling Ambiguous and Unanswerable Questions for Text-to-SQL
Following this study, we propose a simple yet effective counterfactual example generation approach that automatically produces ambiguous and unanswerable text-to-SQL examples.
PATS: Sensitivity-aware Noisy Learning for Pretrained Language Models
A wide range of NLP tasks benefit from the fine-tuning of pretrained language models (PLMs).
LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translation
To this end, we first propose the Multilingual MMT task by establishing two new Multilingual MMT benchmark datasets covering seven languages.
CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation
Specifically, the target sequence is first translated into the source language and then tagged by a source NER model.
Modeling Paragraph-Level Vision-Language Semantic Alignment for Multi-Modal Summarization
The core of ViL-Sum is a joint multi-modal encoder with two well-designed tasks, image reordering and image selection.
LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction
Experiments on five benchmarks validate the effectiveness of LogLG for detecting anomalies on unlabeled log data and demonstrate that LogLG, as the state-of-the-art weakly supervised method, achieves significant performance improvements compared to existing methods.
An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework based on Semantic Blocks
To tackle this problem, in this paper, we proposed an efficient Coarse-to-Fine Facet-Aware Ranking (C2F-FAR) framework for unsupervised long document summarization, which is based on the semantic block.
GTrans: Grouping and Fusing Transformer Layers for Neural Machine Translation
Transformer structure, stacked by a sequence of encoder and decoder network layers, achieves significant development in neural machine translation.
HLT-MT: High-resource Language-specific Training for Multilingual Neural Machine Translation
Nonetheless, multilingual training is plagued by language interference degeneration in shared parameters because of the negative interference among different translation directions, especially on high-resource languages.
UM4: Unified Multilingual Multiple Teacher-Student Model for Zero-Resource Neural Machine Translation
Most translation tasks among languages belong to the zero-resource translation problem where parallel corpora are unavailable.
CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parsers
Context-dependent text-to-SQL is the task of translating multi-turn questions into database-related SQL queries.
TANet: Thread-Aware Pretraining for Abstractive Conversational Summarization
Another is that applying the existing pre-trained models to this task is tricky because of the structural dependence within the conversation and its informal expression, etc.
Modeling Multi-Granularity Hierarchical Features for Relation Extraction
In this paper, we propose a novel method to extract multi-granularity features based solely on the original input sentences.
Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation
The experimental results verify that our proposed NEGCN framework can significantly enhance the performance for various typical GCN models on both node classification and recommendation tasks.
PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation
While end-to-end neural machine translation (NMT) has achieved impressive progress, noisy input usually leads models to become fragile and unstable.
TransLog: A Unified Transformer-based Framework for Log Anomaly Detection
Therefore, we propose a unified Transformer-based framework for log anomaly detection (\ourmethod{}), which is comprised of the pretraining and adapter-based tuning stage.
Jointly Learning to Repair Code and Generate Commit Message
We propose a novel task of jointly repairing program codes and generating commit messages.
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
Text recognition is a long-standing research problem for document digitalization.
Ranked #1 on
Handwritten Text Recognition
on IAM(line-level)
(using extra training data)
Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context
In terms of the local view, we first build a graph structure based on the document where phrases are regarded as vertices and the edges are similarities between vertices.
Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis
Deep person generation has attracted extensive research attention due to its wide applications in virtual agents, video conferencing, online shopping and art/movie production.
Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge of source domain to the unlabeled target domain.
Multilingual Agreement for Multilingual Neural Machine Translation
Although multilingual neural machine translation (MNMT) enables multiple language translations, the training process is based on independent multilingual objectives.
Smart-Start Decoding for Neural Machine Translation
Most current neural machine translation models adopt a monotonic decoding order of either left-to-right or right-to-left.
Non-Recursive Graph Convolutional Networks
In this way, each node can be directly represented by concatenating the information extracted independently from each hop of its neighbors thereby avoiding the recursive neighborhood expansion across layers.
Towards Overcoming False Positives in Visual Relationship Detection
We observe that during training, the relationship proposal distribution is highly imbalanced: most of the negative relationship proposals are easy to identify, e. g., the inaccurate object detection, which leads to the under-fitting of low-frequency difficult proposals.
Formality Style Transfer with Shared Latent Space
Conventional approaches for formality style transfer borrow models from neural machine translation, which typically requires massive parallel data for training.
StyleDGPT: Stylized Response Generation with Pre-trained Language Models
Generating responses following a desired style has great potentials to extend applications of open-domain dialogue systems, yet is refrained by lacking of parallel data for training.
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation Extraction
Then, we propose to predict a subset of potential relations and filter out irrelevant ones to generate questions effectively.
Ranked #1 on
Relation Extraction
on ACE 2005
(Sentence Encoder metric)
Improving Neural Machine Translation with Soft Template Prediction
Although neural machine translation (NMT) has achieved significant progress in recent years, most previous NMT models only depend on the source text to generate translation.
DocBank: A Benchmark Dataset for Document Layout Analysis
DocBank is constructed using a simple yet effective way with weak supervision from the \LaTeX{} documents available on the arXiv. com.
TableBank: Table Benchmark for Image-based Table Detection and Recognition
We present TableBank, a new image-based table detection and recognition dataset built with novel weak supervision from Word and Latex documents on the internet.
Open Domain Dialogue Generation with Latent Images
Thus, we propose learning a response generation model with both image-grounded dialogues and textual dialogues by assuming that the visual scene information at the time of a conversation can be represented by an image, and trying to recover the latent images of the textual dialogues through text-to-image generation techniques.
Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL
One key component in text-to-SQL is to predict the comparison relations between columns and their values.
Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer
Formality text style transfer plays an important role in various NLP applications, such as non-native speaker assistants and child education.
Read, Attend and Comment: A Deep Architecture for Automatic NewsComment Generation
Automatic news comment generation is a new testbed for techniques of natural language generation.
Low-Resource Response Generation with Template Prior
Since the paired data now is no longer enough to train a neural generation model, we consider leveraging the large scale of unpaired data that are much easier to obtain, and propose response generation with both paired and unpaired data.
Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation
Automatic news comment generation is a new testbed for techniques of natural language generation.
Label-Aware Graph Convolutional Networks
In this paper, we consider the problem of node classification and propose the Label-Aware Graph Convolutional Network (LAGCN) framework which can directly identify valuable neighbors to enhance the performance of existing GCN models.
Multi-Hot Compact Network Embedding
Specifically, we propose a MCNE model to learn compact embeddings from pre-learned node features.
TableBank: A Benchmark Dataset for Table Detection and Recognition
We present TableBank, a new image-based table detection and recognition dataset built with novel weak supervision from Word and Latex documents on the internet.
Keyphrase Generation with Correlation Constraints
In this paper, we study automatic keyphrase generation.
TI-CNN: Convolutional Neural Networks for Fake News Detection
By projecting the explicit and latent features into a unified feature space, TI-CNN is trained with both the text and image information simultaneously.
r-Instance Learning for Missing People Tweets Identification
The number of missing people (i. e., people who get lost) greatly increases in recent years.
Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots
We propose a method that can leverage unlabeled data to learn a matching model for response selection in retrieval-based chatbots.
Assertion-based QA with Question-Aware Open Information Extraction
We present assertion based question answering (ABQA), an open domain question answering task that takes a question and a passage as inputs, and outputs a semi-structured assertion consisting of a subject, a predicate and a list of arguments.
Towards Interpretable Chit-chat: Open Domain Dialogue Generation with Dialogue Acts
Conventional methods model open domain dialogue generation as a black box through end-to-end learning from large scale conversation data.
Chinese Answer Extraction Based on POS Tree and Genetic Algorithm
The POS tree is useful to candidate answer extraction of web-based question answering.
Neural Response Generation with Dynamic Vocabularies
We study response generation for open domain conversation in chatbots.
A Sequential Matching Framework for Multi-turn Response Selection in Retrieval-based Chatbots
The task requires matching a response candidate with a conversation context, whose challenges include how to recognize important parts of the context, and how to model the relationships among utterances in the context.
Learning Social Image Embedding with Deep Multimodal Attention Networks
Extensive experiments are conducted to investigate the effectiveness of our approach in the applications of multi-label classification and cross-modal search.
Beihang-MSRA at SemEval-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering
This paper presents the system in SemEval-2017 Task 3, Community Question Answering (CQA).
Content-Based Table Retrieval for Web Queries
Understanding the connections between unstructured text and semi-structured table is an important yet neglected problem in natural language processing.
Jointly Extracting Relations with Class Ties via Effective Deep Ranking
Exploiting class ties between relations of one entity tuple will be promising for distantly supervised relation extraction.
Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots
Existing work either concatenates utterances in context or matches a response with a highly abstract context vector finally, which may lose relationships among utterances or important contextual information.
Ranked #7 on
Conversational Response Selection
on RRS
Knowledge Enhanced Hybrid Neural Network for Text Matching
Long text brings a big challenge to semantic matching due to their complicated semantic and syntactic structures.
Detecting Context Dependent Messages in a Conversational Environment
While automatic response generation for building chatbot systems has drawn a lot of attention recently, there is limited understanding on when we need to consider the linguistic context of an input text in the generation process.
Response Selection with Topic Clues for Retrieval-based Chatbots
The message vector, the response vector, and the two topic vectors are fed to neural tensors to calculate a matching score.
