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Found 191 papers, 106 papers with code
Adaptive Unsupervised Self-training for Disfluency Detection
Supervised methods have achieved remarkable results in disfluency detection.
GL-CLeF: A Global–Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding
Specifically, we employ contrastive learning, leveraging bilingual dictionaries to construct multilingual views of the same utterance, then encourage their representations to be more similar than negative example pairs, which achieves to explicitly align representations of similar sentences across languages.
Don’t be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System
Consistency Identification has obtained remarkable success on open-domain dialogue, which can be used for preventing inconsistent response generation.
CCTC: A Cross-Sentence Chinese Text Correction Dataset for Native Speakers
The Chinese text correction (CTC) focuses on detecting and correcting Chinese spelling errors and grammatical errors.
Simple and Effective Graph-to-Graph Annotation Conversion
Annotation conversion is an effective way to construct datasets under new annotation guidelines based on existing datasets with little human labour.
CGIM: A Cycle Guided Interactive Learning Model for Consistency Identification in Task-oriented Dialogue
Consistency identification in task-oriented dialog (CI-ToD) usually consists of three subtasks, aiming to identify inconsistency between current system response and current user response, dialog history and the corresponding knowledge base.
AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs
Kernel development in deep learning requires optimizing computational units across hardware while balancing memory management, parallelism, and hardware-specific optimizations through extensive empirical tuning.
Advancing Tool-Augmented Large Language Models via Meta-Verification and Reflection Learning
However, current models face two major limitations: (1) unreliable tool planning and invocation due to low-quality instruction datasets (e. g., widespread hallucinated API calls), and (2) weak tool reflection abilities (over 90% of errors cannot be corrected) resulting from static imitation learning.
Think Before You Accept: Semantic Reflective Verification for Faster Speculative Decoding
Using prompt-based probing, we obtain both the original and reflective distributions of draft tokens in a single forward pass.
Lookahead Q-Cache: Achieving More Consistent KV Cache Eviction via Pseudo Query
By using these lookahead queries as the observation window for importance estimation, LAQ achieves more consistent and accurate KV cache eviction aligned with real inference scenarios.
Visual Thoughts: A Unified Perspective of Understanding Multimodal Chain-of-Thought
Additionally, we explore the internal nature of visual thoughts, finding that visual thoughts serve as intermediaries between the input image and reasoning to deeper transformer layers, enabling more advanced visual information transmission.
When Less Language is More: Language-Reasoning Disentanglement Makes LLMs Better Multilingual Reasoners
Multilingual reasoning remains a significant challenge for large language models (LLMs), with performance disproportionately favoring high-resource languages.
RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversals
Recently, reasoning large language models (RLLMs) with Long Chain-of-Thought (Long CoT) significantly enhance reasoning capabilities, leading to brilliant performance on table reasoning.
Success is in the Details: Evaluate and Enhance Details Sensitivity of Code LLMs through Counterfactuals
Code Sensitivity refers to the ability of Code LLMs to recognize and respond to details changes in problem descriptions.
RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning
However, two primary challenges remain for real-world applications: (1) the lack of quantitative metrics and actionable guidelines for evaluating and optimizing measurable boundaries of CoT capability, and (2) the absence of methods to assess boundaries of unmeasurable CoT capability, such as multimodal perception.
Is Compression Really Linear with Code Intelligence?
Understanding the relationship between data compression and the capabilities of Large Language Models (LLMs) is crucial, especially in specialized domains like code intelligence.
AIR: A Systematic Analysis of Annotations, Instructions, and Response Pairs in Preference Dataset
Preference learning is critical for aligning large language models (LLMs) with human values, yet its success hinges on high-quality datasets comprising three core components: Preference \textbf{A}nnotations, \textbf{I}nstructions, and \textbf{R}esponse Pairs.
Trade-offs in Large Reasoning Models: An Empirical Analysis of Deliberative and Adaptive Reasoning over Foundational Capabilities
Recent advancements in Large Reasoning Models (LRMs), such as OpenAI's o1/o3 and DeepSeek-R1, have demonstrated remarkable performance in specialized reasoning tasks through human-like deliberative thinking and long chain-of-thought reasoning.
KwaiChat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus
To mitigate this challenge, we propose a novel task and create a human-to-human video-driven multilingual mixed-type dialogue corpus, termed KwaiChat, containing a total of 93, 209 videos and 246, 080 dialogues, across 4 dialogue types, 30 domains, 4 languages, and 13 topics.
MULTITAT: Benchmarking Multilingual Table-and-Text Question Answering
To align the model TATQA capabilities in English with other languages, we develop a baseline, Ours.
ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation
: (1) Low executability and poor restoration of chart details in the generated code and (2) Lack of large-scale and diverse training data.
Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits
In this paper, we optimize the task by constructing MBTIBench, the first manually annotated high-quality MBTI personality detection dataset with soft labels, under the guidance of psychologists.
CoMT: A Novel Benchmark for Chain of Multi-modal Thought on Large Vision-Language Models
Large Vision-Language Models (LVLMs) have recently demonstrated amazing success in multi-modal tasks, including advancements in Multi-modal Chain-of-Thought (MCoT) reasoning.
SCITAT: A Question Answering Benchmark for Scientific Tables and Text Covering Diverse Reasoning Types
To address these challenges, we propose a QA benchmark for scientific tables and text with diverse reasoning types (SciTaT).
CRVQ: Channel-relaxed Vector Quantization for Extreme Compression of LLMs
Powerful large language models (LLMs) are increasingly expected to be deployed with lower computational costs, enabling their capabilities on resource-constrained devices.
Exploring Multi-Grained Concept Annotations for Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) excel in vision--language tasks by pre-training solely on coarse-grained concept annotations (e. g., image captions).
Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring
Existing black-box jailbreak methods often rely on model feedback, repeatedly submitting queries with detectable malicious instructions during the attack search process.
What Factors Affect Multi-Modal In-Context Learning? An In-Depth Exploration
Recently, rapid advancements in Multi-Modal In-Context Learning (MM-ICL) have achieved notable success, which is capable of achieving superior performance across various tasks without requiring additional parameter tuning.
Unlocking the Capabilities of Thought: A Reasoning Boundary Framework to Quantify and Optimize Chain-of-Thought
Chain-of-Thought (CoT) reasoning has emerged as a promising approach for enhancing the performance of large language models (LLMs) on complex reasoning tasks.
Lens: Rethinking Multilingual Enhancement for Large Language Models
Despite the growing global demand for large language models (LLMs) that serve users from diverse linguistic backgrounds, most cutting-edge LLMs remain predominantly English-centric.
In-Context Transfer Learning: Demonstration Synthesis by Transferring Similar Tasks
To address this, inspired by transfer learning, we propose In-Context Transfer Learning (ICTL), which synthesizes target task demonstrations by transferring labeled demonstrations from similar source tasks.
Enabling Real-Time Conversations with Minimal Training Costs
To address this limitation, researchers have proposed duplex models.
What are the Essential Factors in Crafting Effective Long Context Multi-Hop Instruction Datasets? Insights and Best Practices
In order to achieve success in long context tasks, a large amount of work has been done to enhance the long context capabilities of the model through synthetic data.
DAC: Decomposed Automation Correction for Text-to-SQL
Therefore, in this paper, we propose to employ the decomposed correction to enhance text-to-SQL performance.
FLEXTAF: Enhancing Table Reasoning with Flexible Tabular Formats
Most existing methods employ a fixed tabular format to represent the table, which could limit the performance.
Concise and Precise Context Compression for Tool-Using Language Models
However, when compressing tool documentation, existing methods suffer from the weaknesses of key information loss (specifically, tool/parameter name errors) and difficulty in adjusting the length of compressed sequences based on documentation lengths.
CVLUE: A New Benchmark Dataset for Chinese Vision-Language Understanding Evaluation
Our in-depth category-level analysis reveals a lack of Chinese cultural knowledge in existing VLMs.
Improving Grammatical Error Correction via Contextual Data Augmentation
Nowadays, data augmentation through synthetic data has been widely used in the field of Grammatical Error Correction (GEC) to alleviate the problem of data scarcity.
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training
To address this problem, we propose the Make Some Noise (MSN) training framework as a replacement for the supervised fine-tuning stage of the large language model.
Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement
Decompilation transforms compiled code back into a high-level programming language for analysis when source code is unavailable.
AutoCAP: Towards Automatic Cross-lingual Alignment Planning for Zero-shot Chain-of-Thought
Cross-lingual chain-of-thought can effectively complete reasoning tasks across languages, which gains increasing attention.
CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding
To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU.
Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey
Compared to traditional sentiment analysis, which only considers text, multimodal sentiment analysis needs to consider emotional signals from multimodal sources simultaneously and is therefore more consistent with the way how humans process sentiment in real-world scenarios.
M$^3$CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought
In addition, we highlight that the current VLLMs still struggle to correctly reason in M$^3$CoT and there remains a large gap between existing VLLMs and human performance in M$^3$CoT, despite their superior results on previous MCoT benchmarks.
Large Language Models Meet NLP: A Survey
While large language models (LLMs) like ChatGPT have shown impressive capabilities in Natural Language Processing (NLP) tasks, a systematic investigation of their potential in this field remains largely unexplored.
Improving Language Model Reasoning with Self-motivated Learning
The framework motivates the model itself to automatically generate rationales on existing datasets.
Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers
To this end, in this paper, we present a thorough review and provide a unified perspective to summarize the recent progress as well as emerging trends in multilingual large language models (MLLMs) literature.
Against The Achilles' Heel: A Survey on Red Teaming for Generative Models
Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safe use as various vulnerabilities are exposed.
LM-Combiner: A Contextual Rewriting Model for Chinese Grammatical Error Correction
In this light, we propose the LM-Combiner, a rewriting model that can directly modify the over-correction of GEC system outputs without a model ensemble.
Beyond Static Evaluation: A Dynamic Approach to Assessing AI Assistants' API Invocation Capabilities
With the rise of Large Language Models (LLMs), AI assistants' ability to utilize tools, especially through API calls, has advanced notably.
Semi-Instruct: Bridging Natural-Instruct and Self-Instruct for Code Large Language Models
Presently, two dominant paradigms for collecting tuning data are natural-instruct (human-written) and self-instruct (automatically generated).
OneBit: Towards Extremely Low-bit Large Language Models
Model quantification uses low bit-width values to represent the weight matrices of existing models to be quantized, which is a promising approach to reduce both storage and computational overheads of deploying highly anticipated LLMs.
Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts
In this paper, we conduct comprehensive experiments on the programming languages used in PoT and find that no single language consistently delivers optimal performance across all tasks and models.
Exploring Hybrid Question Answering via Program-based Prompting
Question answering over heterogeneous data requires reasoning over diverse sources of data, which is challenging due to the large scale of information and organic coupling of heterogeneous data.
MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQL
The open-domain text-to-SQL task aims to retrieve question-relevant tables from massive databases and generate SQL.
Enhancing Numerical Reasoning with the Guidance of Reliable Reasoning Processes
Numerical reasoning is an essential ability for NLP systems to handle numeric information.
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL
Currently, the in-context learning method based on large language models (LLMs) has become the mainstream of text-to-SQL research.
A Survey of Table Reasoning with Large Language Models
In this paper, we analyze the mainstream techniques used to improve table reasoning performance in the LLM era, and the advantages of LLMs compared to pre-LLMs for solving table reasoning.
Pro-HAN: A Heterogeneous Graph Attention Network for Profile-Based Spoken Language Understanding
Recently, Profile-based Spoken Language Understanding (SLU) has gained increasing attention, which aims to incorporate various types of supplementary profile information (i. e., Knowledge Graph, User Profile, Context Awareness) to eliminate the prevalent ambiguities in user utterances.
SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models
Existing methods devise the learning module to acquire task-specific knowledge with parameter-efficient tuning (PET) block and the selection module to pick out the corresponding one for the testing input, aiming at handling the challenges of catastrophic forgetting and knowledge transfer in CL.
End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions
End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity.
Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages
The cross-lingual alignment prompting is responsible for aligning representations across different languages, whereas the task-specific solver prompting is used to generate the final chain of thoughts and results for the reasoning task.
Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue
This paper investigates the effectiveness of combining LLM and CRS in E-commerce pre-sales dialogues, proposing two collaboration methods: CRS assisting LLM and LLM assisting CRS.
Exploring Equation as a Better Intermediate Meaning Representation for Numerical Reasoning
So in this paper, we try to use equations as IMRs to solve the numerical reasoning task by addressing two problems: (1) Theoretically, how to prove that the equation is an IMR with higher generation accuracy than programs; (2) Empirically, how to improve the generation accuracy of equations with LLMs.
MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System
Multi-modal sarcasm detection has attracted much recent attention.
MetricPrompt: Prompting Model as a Relevance Metric for Few-shot Text Classification
Despite the promising prospects, the performance of prompting model largely depends on the design of prompt template and verbalizer.
ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images
It is hoped that this competition will attract many researchers in the field of CV and NLP, and bring some new thoughts to the field of Document AI.
ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning
With only 4M VLP data, ManagerTower achieves superior performances on various downstream VL tasks, especially 79. 15% accuracy on VQAv2 Test-Std, 86. 56% IR@1 and 95. 64% TR@1 on Flickr30K.
OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding
Spoken Language Understanding (SLU) is one of the core components of a task-oriented dialogue system, which aims to extract the semantic meaning of user queries (e. g., intents and slots).
CSED: A Chinese Semantic Error Diagnosis Corpus
Our experiments show that powerful pre-trained models perform poorly on this corpus.
U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation
In this paper, we construct a user needs-centric E-commerce conversational recommendation dataset (U-NEED) from real-world E-commerce scenarios.
Controllable Data Augmentation for Context-Dependent Text-to-SQL
In this paper, we introduce ConDA, which generates interactive questions and corresponding SQL results.
MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning
Prompt-based learning has shown considerable promise in reformulating various downstream tasks as cloze problems by combining original input with a predetermined template.
A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification
Cross-domain text classification aims to adapt models to a target domain that lacks labeled data.
A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding
Zero-shot dialogue understanding aims to enable dialogue to track the user's needs without any training data, which has gained increasing attention.
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification
In contrast, generative methods bring more image diversity in the augmented images but may not preserve semantic consistency, thus incorrectly changing the essential semantics of the original image.
HIT-SCIR at MMNLU-22: Consistency Regularization for Multilingual Spoken Language Understanding
Multilingual spoken language understanding (SLU) consists of two sub-tasks, namely intent detection and slot filling.
Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge
In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables.
MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing
Text-to-SQL semantic parsing is an important NLP task, which greatly facilitates the interaction between users and the database and becomes the key component in many human-computer interaction systems.
A Survey on Table-and-Text HybridQA: Concepts, Methods, Challenges and Future Directions
Table-and-text hybrid question answering (HybridQA) is a widely used and challenging NLP task commonly applied in the financial and scientific domain.
A Survey on Natural Language Processing for Programming
Natural language processing for programming aims to use NLP techniques to assist programming.
LERT: A Linguistically-motivated Pre-trained Language Model
We propose LERT, a pre-trained language model that is trained on three types of linguistic features along with the original MLM pre-training task, using a linguistically-informed pre-training (LIP) strategy.
Ranked #6 on
Stock Market Prediction
on Astock
MetaPrompting: Learning to Learn Better Prompts
Prompting method is regarded as one of the crucial progress for few-shot nature language processing.
Overview of CTC 2021: Chinese Text Correction for Native Speakers
In this paper, we present an overview of the CTC 2021, a Chinese text correction task for native speakers.
BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning
Vision-Language (VL) models with the Two-Tower architecture have dominated visual-language representation learning in recent years.
Language Anisotropic Cross-Lingual Model Editing
On the newly defined cross-lingual model editing task, we empirically demonstrate the failure of monolingual baselines in propagating the edit to multiple languages and the effectiveness of the proposed language anisotropic model editing.
GL-CLeF: A Global-Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding
We present Global--Local Contrastive Learning Framework (GL-CLeF) to address this shortcoming.
Improving Pre-trained Language Models with Syntactic Dependency Prediction Task for Chinese Semantic Error Recognition
These errors have been studied extensively and are relatively simple for humans.
Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging
Such inverse prompting only requires a one-turn prediction for each slot type and greatly speeds up the prediction.
UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL
Existing text-to-SQL semantic parsers are typically designed for particular settings such as handling queries that span multiple tables, domains or turns which makes them ineffective when applied to different settings.
InterHT: Knowledge Graph Embeddings by Interaction between Head and Tail Entities
Knowledge graph embedding (KGE) models learn the representation of entities and relations in knowledge graphs.
Ranked #4 on
Link Property Prediction
on ogbl-wikikg2
TextHacker: Learning based Hybrid Local Search Algorithm for Text Hard-label Adversarial Attack
Existing textual adversarial attacks usually utilize the gradient or prediction confidence to generate adversarial examples, making it hard to be deployed in real-world applications.
Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding
Current researches on spoken language understanding (SLU) heavily are limited to a simple setting: the plain text-based SLU that takes the user utterance as input and generates its corresponding semantic frames (e. g., intent and slots).
Ranked #1 on
Semantic Frame Parsing
on ProSLU
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Data Augmentation Approaches in Natural Language Processing: A Survey
One of the main focuses of the DA methods is to improve the diversity of training data, thereby helping the model to better generalize to unseen testing data.
Discovering Drug-Target Interaction Knowledge from Biomedical Literature
We regard the DTI triplets as a sequence and use a Transformer-based model to directly generate them without using the detailed annotations of entities and relations.
Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System
Consistency Identification has obtained remarkable success on open-domain dialogue, which can be used for preventing inconsistent response generation.
DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation
In this paper, we provide a bilingual parallel human-to-human recommendation dialog dataset (DuRecDial 2. 0) to enable researchers to explore a challenging task of multilingual and cross-lingual conversational recommendation.
Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-training
We find that many languages are under-represented in recent cross-lingual language models due to the limited vocabulary capacity.
Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension Models
Achieving human-level performance on some of the Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs).
Discovering Dialog Structure Graph for Coherent Dialog Generation
Learning discrete dialog structure graph from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation.
Consistency Regularization for Cross-Lingual Fine-Tuning
Fine-tuning pre-trained cross-lingual language models can transfer task-specific supervision from one language to the others.
Adversarial Training for Machine Reading Comprehension with Virtual Embeddings
Adversarial training (AT) as a regularization method has proved its effectiveness on various tasks.
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual Transfer
Multilingual pre-trained models have achieved remarkable performance on cross-lingual transfer learning.
GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling
Multi-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention.
Ranked #10 on
Slot Filling
on MixATIS
Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling
However, dialogue language understanding contains two closely related tasks, i. e., intent detection and slot filling, and often benefits from jointly learning the two tasks.
ExpMRC: Explainability Evaluation for Machine Reading Comprehension
Achieving human-level performance on some of Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs).
Ranked #1 on
Multi-Choice MRC
on ExpMRC - RACE+ (test)
A Survey on Spoken Language Understanding: Recent Advances and New Frontiers
Spoken Language Understanding (SLU) aims to extract the semantics frame of user queries, which is a core component in a task-oriented dialog system.
Discovering Dialog Structure Graph for Open-Domain Dialog Generation
Learning interpretable dialog structure from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation.
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents.
Ranked #1 on
Key Information Extraction
on SROIE
Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification
The dialog context information (contextual information) and the mutual interaction information are two key factors that contribute to the two related tasks.
C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot Filling
Slot filling, a fundamental module of spoken language understanding, often suffers from insufficient quantity and diversity of training data.
HIT-SCIR at MRP 2020: Transition-based Parser and Iterative Inference Parser
This paper describes our submission system (HIT-SCIR) for the CoNLL 2020 shared task: Cross-Framework and Cross-Lingual Meaning Representation Parsing.
Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection
Most existing approaches to disfluency detection heavily rely on human-annotated corpora, which is expensive to obtain in practice.
Few-shot Learning for Multi-label Intent Detection
In this paper, we study the few-shot multi-label classification for user intent detection.
A Co-Interactive Transformer for Joint Slot Filling and Intent Detection
Instead of adopting the self-attention mechanism in vanilla Transformer, we propose a co-interactive module to consider the cross-impact by building a bidirectional connection between the two related tasks.
Injecting Word Information with Multi-Level Word Adapter for Chinese Spoken Language Understanding
In this paper, we improve Chinese spoken language understanding (SLU) by injecting word information.
Combining ResNet and Transformer for Chinese Grammatical Error Diagnosis
Grammatical error diagnosis is an important task in natural language processing.
N-LTP: An Open-source Neural Language Technology Platform for Chinese
We introduce \texttt{N-LTP}, an open-source neural language technology platform supporting six fundamental Chinese NLP tasks: {lexical analysis} (Chinese word segmentation, part-of-speech tagging, and named entity recognition), {syntactic parsing} (dependency parsing), and {semantic parsing} (semantic dependency parsing and semantic role labeling).
FewJoint: A Few-shot Learning Benchmark for Joint Language Understanding
In this paper, we present FewJoint, a novel Few-Shot Learning benchmark for NLP.
DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification
In dialog system, dialog act recognition and sentiment classification are two correlative tasks to capture speakers intentions, where dialog act and sentiment can indicate the explicit and the implicit intentions separately.
Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation
To address the challenge of policy learning in open-domain multi-turn conversation, we propose to represent prior information about dialog transitions as a graph and learn a graph grounded dialog policy, aimed at fostering a more coherent and controllable dialog.
Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network
Data-driven approaches using neural networks have achieved promising performances in natural language generation (NLG).
CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP
Compared with the existing work, our method does not rely on bilingual sentences for training, and requires only one training process for multiple target languages.
Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network
In this paper, we explore the slot tagging with only a few labeled support sentences (a. k. a.
Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension
Natural Questions is a new challenging machine reading comprehension benchmark with two-grained answers, which are a long answer (typically a paragraph) and a short answer (one or more entities inside the long answer).
Towards Conversational Recommendation over Multi-Type Dialogs
We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e. g., QA) to a recommendation dialog, taking into account user's interests and feedback.
Multi-Domain Spoken Language Understanding Using Domain- and Task-Aware Parameterization
Spoken language understanding has been addressed as a supervised learning problem, where a set of training data is available for each domain.
Revisiting Pre-Trained Models for Chinese Natural Language Processing
Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pre-trained language models.
Ranked #13 on
Stock Market Prediction
on Astock
Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting
Deep pretrained language models have achieved great success in the way of pretraining first and then fine-tuning.
Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog
However, there has been relatively little research on how to effectively use data from all domains to improve the performance of each domain and also unseen domains.
Ranked #1 on
Task-Oriented Dialogue Systems
on Kvret
AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
Such an interaction layer is applied to each token adaptively, which has the advantage to automatically extract the relevant intents information, making a fine-grained intent information integration for the token-level slot prediction.
Ranked #4 on
Intent Detection
on SNIPS
A Sentence Cloze Dataset for Chinese Machine Reading Comprehension
To add diversity in this area, in this paper, we propose a new task called Sentence Cloze-style Machine Reading Comprehension (SC-MRC).
TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing
In this paper, we introduce TextBrewer, an open-source knowledge distillation toolkit designed for natural language processing.
Discriminative Sentence Modeling for Story Ending Prediction
Story Ending Prediction is a task that needs to select an appropriate ending for the given story, which requires the machine to understand the story and sometimes needs commonsense knowledge.
Contextual Recurrent Units for Cloze-style Reading Comprehension
Recurrent Neural Networks (RNN) are known as powerful models for handling sequential data, and especially widely utilized in various natural language processing tasks.
Improving Machine Reading Comprehension via Adversarial Training
With virtual adversarial training (VAT), we explore the possibility of improving the RC models with semi-supervised learning and prove that examples from a different task are also beneficial.
HIT-SCIR at MRP 2019: A Unified Pipeline for Meaning Representation Parsing via Efficient Training and Effective Encoding
This paper describes our system (HIT-SCIR) for CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing.
Ranked #1 on
UCCA Parsing
on CoNLL 2019
Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing
In this approach, a linear transformation is learned from contextual word alignments to align the contextualized embeddings independently trained in different languages.
Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever
Querying the knowledge base (KB) has long been a challenge in the end-to-end task-oriented dialogue system.
Ranked #6 on
Task-Oriented Dialogue Systems
on KVRET
A Corpus-free State2Seq User Simulator for Task-oriented Dialogue
The framework builds a user simulator by first generating diverse dialogue data from templates and then build a new State2Seq user simulator on the data.
A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding
In our framework, we adopt a joint model with Stack-Propagation which can directly use the intent information as input for slot filling, thus to capture the intent semantic knowledge.
Ranked #2 on
Intent Detection
on SNIPS
Cross-Lingual Machine Reading Comprehension
In this paper, we propose Cross-Lingual Machine Reading Comprehension (CLMRC) task for the languages other than English.
Multi-Task Self-Supervised Learning for Disfluency Detection
The pre-trained network is then fine-tuned using human-annotated disfluency detection training data.
Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency
Experiments on three popular datasets using convolutional as well as LSTM models show that PWWS reduces the classification accuracy to the most extent, and keeps a very low word substitution rate.
Few-Shot Sequence Labeling with Label Dependency Transfer and Pair-wise Embedding
It calculates emission score with similarity based methods and obtains transition score with a specially designed transfer mechanism.
Pre-Training with Whole Word Masking for Chinese BERT
To demonstrate the effectiveness of these models, we create a series of Chinese pre-trained language models as our baselines, including BERT, RoBERTa, ELECTRA, RBT, etc.
A Span-Extraction Dataset for Chinese Machine Reading Comprehension
Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention.
An AMR Aligner Tuned by Transition-based Parser
In this paper, we propose a new rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser.
Ranked #2 on
AMR Parsing
on LDC2014T12:
Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation
This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies.
Ranked #3 on
Dependency Parsing
on Universal Dependencies
Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding
In this paper, we study the problem of data augmentation for language understanding in task-oriented dialogue system.
Chinese Grammatical Error Diagnosis using Statistical and Prior Knowledge driven Features with Probabilistic Ensemble Enhancement
This paper describes our system at NLPTEA-2018 Task {\#}1: Chinese Grammatical Error Diagnosis.
Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation
Classic pipeline models for task-oriented dialogue system require explicit modeling the dialogue states and hand-crafted action spaces to query a domain-specific knowledge base.
Ranked #7 on
Task-Oriented Dialogue Systems
on KVRET
Distilling Knowledge for Search-based Structured Prediction
Many natural language processing tasks can be modeled into structured prediction and solved as a search problem.
Parsing Tweets into Universal Dependencies
Nonetheless, using the new treebank, we build a pipeline system to parse raw tweets into UD.
Ranked #2 on
Dependency Parsing
on Tweebank
Deep Learning in Lexical Analysis and Parsing
Neural networks, also with a fancy name deep learning, just right can overcome the above {``}feature engineering{''} problem.
The First Evaluation of Chinese Human-Computer Dialogue Technology
In this paper, we introduce the first evaluation of Chinese human-computer dialogue technology.
Transition-Based Disfluency Detection using LSTMs
In this paper, we model the problem of disfluency detection using a transition-based framework, which incrementally constructs and labels the disfluency chunk of input sentences using a new transition system without syntax information.
The HIT-SCIR System for End-to-End Parsing of Universal Dependencies
Our system includes three pipelined components: \textit{tokenization}, \textit{Part-of-Speech} (POS) \textit{tagging} and \textit{dependency parsing}.
A Unified Architecture for Semantic Role Labeling and Relation Classification
This paper describes a unified neural architecture for identifying and classifying multi-typed semantic relations between words in a sentence.
A Neural Attention Model for Disfluency Detection
We treat disfluency detection as a sequence-to-sequence problem and propose a neural attention-based model which can efficiently model the long-range dependencies between words and make the resulting sentence more likely to be grammatically correct.
A Universal Framework for Inductive Transfer Parsing across Multi-typed Treebanks
Various treebanks have been released for dependency parsing.
Chinese Grammatical Error Diagnosis with Long Short-Term Memory Networks
This paper introduces our Chinese Grammatical Error Diagnosis (CGED) system in the NLP-TEA-3 shared task for CGED.
Exploiting Multi-typed Treebanks for Parsing with Deep Multi-task Learning
Various treebanks have been released for dependency parsing.
Exploring Segment Representations for Neural Segmentation Models
Many natural language processing (NLP) tasks can be generalized into segmentation problem.
A Representation Learning Framework for Multi-Source Transfer Parsing
Cross-lingual model transfer has been a promising approach for inducing dependency parsers for low-resource languages where annotated treebanks are not available.
Cross-lingual zero-shot dependency parsing
Representation Learning
