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Found 44 papers, 22 papers with code
Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Cluster for Extreme Multi-label Text Classification
Extreme multi-label text classification (XMTC) is a task for tagging a given text with the most relevant labels from an extremely large label set.
Multi Label Text Classification
Multi-Label Text Classification
+1
Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data
Large Language Models (LLMs) operating in 0-shot or few-shot settings achieve competitive results in Text Classification tasks.
A Framework for Cost-Effective and Self-Adaptive LLM Shaking and Recovery Mechanism
As Large Language Models (LLMs) gain great success in real-world applications, an increasing number of users are seeking to develop and deploy their customized LLMs through cloud services.
RecDCL: Dual Contrastive Learning for Recommendation
In this work, we investigate how to employ both batch-wise CL (BCL) and feature-wise CL (FCL) for recommendation.
Dependency Relationships-Enhanced Attentive Group Recommendation in HINs
Finally, we develop an attention aggregator that aggregates users' preferences as the group's preferences for the group recommendation task.
InstructPTS: Instruction-Tuning LLMs for Product Title Summarization
E-commerce product catalogs contain billions of items.
MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition
We present MULTICONER V2, a dataset for fine-grained Named Entity Recognition covering 33 entity classes across 12 languages, in both monolingual and multilingual settings.
Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting
Mental illness remains one of the most critical public health issues of our time, due to the severe scarcity and accessibility limit of professionals.
Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search
We propose an intent-aware FAQ retrieval system consisting of (1) an intent classifier that predicts when a user's information need can be answered by an FAQ; (2) a reformulation model that rewrites a query into a natural question.
Answering Unanswered Questions through Semantic Reformulations in Spoken QA
Spoken Question Answering (QA) is a key feature of voice assistants, usually backed by multiple QA systems.
Unconfounded Propensity Estimation for Unbiased Ranking
The goal of unbiased learning to rank (ULTR) is to leverage implicit user feedback for optimizing learning-to-rank systems.
SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)
The task highlights the need for future research on improving NER robustness on noisy data containing complex entities.
Multilingual Named Entity Recognition
named-entity-recognition
+3
Multimodal Procedural Planning via Dual Text-Image Prompting
The key challenges of MPP are to ensure the informativeness, temporal coherence, and accuracy of plans across modalities.
Reinforced Question Rewriting for Conversational Question Answering
Furthermore, human evaluation shows that our method can generate more accurate and detailed rewrites when compared to human annotations.
Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling
Our method first addresses irregularity in each single modality by (1) modeling irregular time series by dynamically incorporating hand-crafted imputation embeddings into learned interpolation embeddings via a gating mechanism, and (2) casting a series of clinical note representations as multivariate irregular time series and tackling irregularity via a time attention mechanism.
Explanations from Large Language Models Make Small Reasoners Better
Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations.
ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering
With the recent advance in large pre-trained language models, researchers have achieved record performances in NLP tasks that mostly focus on language pattern matching.
Ranked #2 on
Question Answering
on ConvFinQA
PIM-QAT: Neural Network Quantization for Processing-In-Memory (PIM) Systems
In this paper, we propose a method for training quantized networks to incorporate PIM quantization, which is ubiquitous to all PIM systems.
Model-based Unbiased Learning to Rank
Existing methods in unbiased learning to rank typically rely on click modeling or inverse propensity weighting (IPW).
KETOD: Knowledge-Enriched Task-Oriented Dialogue
We also propose two new models, SimpleToDPlus and Combiner, for the proposed task.
StruBERT: Structure-aware BERT for Table Search and Matching
In this paper, we propose StruBERT, a structure-aware BERT model that fuses the textual and structural information of a data table to produce context-aware representations for both textual and tabular content of a data table.
F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization
Our approach achieves comparable and better performance, when compared not only to existing quantization techniques with INT32 multiplication or floating-point arithmetic, but also to the full-precision counterparts, achieving state-of-the-art performance.
FinQA: A Dataset of Numerical Reasoning over Financial Data
In contrast to existing tasks on general domain, the finance domain includes complex numerical reasoning and understanding of heterogeneous representations.
Ranked #4 on
Question Answering
on FinQA
Programmable FPGA-based Memory Controller
Since developing memory controllers for different applications is time-consuming, this paper introduces a modular and programmable memory controller that can be configured for different target applications on available hardware resources.
CAP-RAM: A Charge-Domain In-Memory Computing 6T-SRAM for Accurate and Precision-Programmable CNN Inference
A compact, accurate, and bitwidth-programmable in-memory computing (IMC) static random-access memory (SRAM) macro, named CAP-RAM, is presented for energy-efficient convolutional neural network (CNN) inference.
Automatic Head Overcoat Thickness Measure with NASNet-Large-Decoder Net
To further improve segmentation results, we are the first to propose a post-processing layer to remove irrelevant portions in the segmentation result.
Local Aggressive Adversarial Attacks on 3D Point Cloud
However, the perturbations of global point are not effective for misleading the victim model.
WTR: A Test Collection for Web Table Retrieval
We describe the development, characteristics and availability of a test collection for the task of Web table retrieval, which uses a large-scale Web Table Corpora extracted from the Common Crawl.
Inductive Relation Prediction by BERT
Relation prediction in knowledge graphs is dominated by embedding based methods which mainly focus on the transductive setting.
Neural ranking models for document retrieval
A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking.
NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration
With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed.
NUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions
As there is no clean mapping for a user's free form utterance to an ontology, we first model the user preferences as estimated distributions over the system ontology and map the users' utterances to such distributions.
Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification
Extreme multi-label text classification (XMTC) is a task for tagging a given text with the most relevant labels from an extremely large label set.
Table Search Using a Deep Contextualized Language Model
Pretrained contextualized language models such as BERT have achieved impressive results on various natural language processing benchmarks.
Logic2Text: High-Fidelity Natural Language Generation from Logical Forms
If only provided with the table, it is hard for existing models to produce controllable and high-fidelity logical generations.
Logical Natural Language Generation from Open-Domain Tables
To facilitate the study of the proposed logical NLG problem, we use the existing TabFact dataset \cite{chen2019tabfact} featured with a wide range of logical/symbolic inferences as our testbed, and propose new automatic metrics to evaluate the fidelity of generation models w. r. t.\ logical inference.
HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data
3) a hybrid model that combines heterogeneous information to find the answer.
Ranked #4 on
Question Answering
on HybridQA
HULK: An Energy Efficiency Benchmark Platform for Responsible Natural Language Processing
We introduce HULK, a multi-task energy efficiency benchmarking platform for responsible natural language processing.
Towards the standardization of quantum state verification using optimal strategies
We experimentally obtain the scaling parameter of $r=-0. 88\pm$0. 03 and $-0. 78\pm$0. 07 for nonadaptive and adaptive strategies, respectively.
Quantum Physics Optics
Leveraging Schema Labels to Enhance Dataset Search
We incorporate the generated schema labels into a mixed ranking model which not only considers the relevance between the query and dataset metadata but also the similarity between the query and generated schema labels.
Global Textual Relation Embedding for Relational Understanding
Pre-trained embeddings such as word embeddings and sentence embeddings are fundamental tools facilitating a wide range of downstream NLP tasks.
Ranked #142 on
Action Classification
on Kinetics-400
Few-Shot NLG with Pre-Trained Language Model
Neural-based end-to-end approaches to natural language generation (NLG) from structured data or knowledge are data-hungry, making their adoption for real-world applications difficult with limited data.
How Large a Vocabulary Does Text Classification Need? A Variational Approach to Vocabulary Selection
Under deep neural networks, a pre-defined vocabulary is required to vectorize text inputs.
Mask Editor : an Image Annotation Tool for Image Segmentation Tasks
Mask Editor allows drawing any bounding curve to mark objects and improves efficiency to mark objects with irregular shapes.
