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Found 27 papers, 15 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
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.
PIM-QAT: Neural Network Quantization For Processing-In-Memory (PIM) Systems
However, an extra quantization step (i. e. PIM quantization), typically with limited resolution due to hardware constraints, is required to convert the analog computing results into digital domain.
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 #1 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.
Multi Label Text Classification
Multi-Label Text Classification
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 #88 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.
