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Found 118 papers, 46 papers with code
A Knowledge storage and semantic space alignment Method for Multi-documents dialogue generation
Question Answering (QA) is a Natural Language Processing (NLP) task that can measure language and semantics understanding ability, it requires a system not only to retrieve relevant documents from a large number of articles but also to answer corresponding questions according to documents.
Improving Relation Extraction through Syntax-induced Pre-training with Dependency Masking
Relation extraction (RE) is an important natural language processing task that predicts the relation between two given entities, where a good understanding of the contextual information is essential to achieve an outstanding model performance.
Ranked #11 on
Relation Extraction
on SemEval-2010 Task-8
Enhancing Structure-aware Encoder with Extremely Limited Data for Graph-based Dependency Parsing
Dependency parsing is an important fundamental natural language processing task which analyzes the syntactic structure of an input sentence by illustrating the syntactic relations between words.
Ranked #2 on
Dependency Parsing
on Penn Treebank
Syntax-driven Approach for Semantic Role Labeling
To achieve a better performance in SRL, a model is always required to have a good understanding of the context information.
Ranked #2 on
Semantic Role Labeling
on CoNLL 2005
Complementary Learning of Aspect Terms for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity towards a given aspect term in a sentence on the fine-grained level, which usually requires a good understanding of contextual information, especially appropriately distinguishing of a given aspect and its contexts, to achieve good performance.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
ChiMST: A Chinese Medical Corpus for Word Segmentation and Medical Term Recognition
Chinese word segmentation (CWS) and named entity recognition (NER) are two important tasks in Chinese natural language processing.
LingJing at SemEval-2022 Task 3: Applying DeBERTa to Lexical-level Presupposed Relation Taxonomy with Knowledge Transfer
For the classification sub-task, we adopt the DeBERTa-v3 pre-trained model for fine-tuning datasets of different languages.
VPAI_Lab at MedVidQA 2022: A Two-Stage Cross-modal Fusion Method for Medical Instructional Video Classification
Given an input video, the MedVidCL task aims to correctly classify it into one of three following categories: Medical Instructional, Medical Non-instructional, and Non-medical.
ReferIt3D: Neural Listeners for Fine-Grained 3D Object Identification in Real-World Scenes
Due to the scarcity and unsuitability of existent 3D-oriented linguistic resources for this task, we first develop two large-scale and complementary visio-linguistic datasets: i) extbf{ extit{Sr3D}}, which contains 83. 5K template-based utterances leveraging extit{spatial relations} with other fine-grained object classes to localize a referred object in a given scene, and ii) extbf{ extit{Nr3D}} which contains 41. 5K extit{natural, free-form}, utterances collected by deploying a 2-player object reference game in 3D scenes.
LingJing at SemEval-2022 Task 1: Multi-task Self-supervised Pre-training for Multilingual Reverse Dictionary
This paper introduces the approach of Team LingJing’s experiments on SemEval-2022 Task 1 Comparing Dictionaries and Word Embeddings (CODWOE).
GenCHiP: Generating Robot Policy Code for High-Precision and Contact-Rich Manipulation Tasks
Large Language Models (LLMs) have been successful at generating robot policy code, but so far these results have been limited to high-level tasks that do not require precise movement.
Extracting Social Determinants of Health from Pediatric Patient Notes Using Large Language Models: Novel Corpus and Methods
In this work, we present a novel annotated corpus, the Pediatric Social History Annotation Corpus (PedSHAC), and evaluate the automatic extraction of detailed SDoH representations using fine-tuned and in-context learning methods with Large Language Models (LLMs).
MicroDiffusion: Implicit Representation-Guided Diffusion for 3D Reconstruction from Limited 2D Microscopy Projections
This strategy enriches the diffusion process with structured 3D information, enhancing detail and reducing noise in localized 2D images.
BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation
We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered robotics.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
In this report, we present the latest model of the Gemini family, Gemini 1. 5 Pro, a highly compute-efficient multimodal mixture-of-experts model capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio.
Ranked #19 on
Code Generation
on HumanEval
PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs
In each iteration, the image is annotated with a visual representation of proposals that the VLM can refer to (e. g., candidate robot actions, localizations, or trajectories).
AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents
We experimentally show that such "in-the-wild" data collected by AutoRT is significantly more diverse, and that AutoRT's use of LLMs allows for instruction following data collection robots that can align to human preferences.
SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities
By training a VLM on such data, we significantly enhance its ability on both qualitative and quantitative spatial VQA.
Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis
Motivated by the impressive open-set performance and content generation capabilities of web-scale, large-capacity pre-trained models (i. e., foundation models) in research fields such as Natural Language Processing (NLP) and Computer Vision (CV), we devote this survey to exploring (i) how these existing foundation models from NLP and CV can be applied to the field of robotics, and also exploring (ii) what a robotics-specific foundation model would look like.
Chain of Code: Reasoning with a Language Model-Augmented Code Emulator
For example, consider prompting an LM to write code that counts the number of times it detects sarcasm in an essay: the LM may struggle to write an implementation for "detect_sarcasm(string)" that can be executed by the interpreter (handling the edge cases would be insurmountable).
Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future
With the continuous maturation and application of autonomous driving technology, a systematic examination of open-source autonomous driving datasets becomes instrumental in fostering the robust evolution of the industry ecosystem.
Distilling and Retrieving Generalizable Knowledge for Robot Manipulation via Language Corrections
DROC is able to respond to a sequence of online language corrections that address failures in both high-level task plans and low-level skill primitives.
Creative Robot Tool Use with Large Language Models
This paper investigates the feasibility of imbuing robots with the ability to creatively use tools in tasks that involve implicit physical constraints and long-term planning.
Video Language Planning
We are interested in enabling visual planning for complex long-horizon tasks in the space of generated videos and language, leveraging recent advances in large generative models pretrained on Internet-scale data.
Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning
Navigation in unfamiliar environments presents a major challenge for robots: while mapping and planning techniques can be used to build up a representation of the world, quickly discovering a path to a desired goal in unfamiliar settings with such methods often requires lengthy mapping and exploration.
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions
In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human demonstrations and autonomously collected data.
Physically Grounded Vision-Language Models for Robotic Manipulation
We incorporate this physically grounded VLM in an interactive framework with a large language model-based robotic planner, and show improved planning performance on tasks that require reasoning about physical object concepts, compared to baselines that do not leverage physically grounded VLMs.
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web.
Deep Learning with Passive Optical Nonlinear Mapping
Utilizing rapid optical information processing capabilities, our optical platforms could potentially offer more efficient and real-time processing solutions for a broad range of applications.
Large Language Models as General Pattern Machines
We observe that pre-trained large language models (LLMs) are capable of autoregressively completing complex token sequences -- from arbitrary ones procedurally generated by probabilistic context-free grammars (PCFG), to more rich spatial patterns found in the Abstraction and Reasoning Corpus (ARC), a general AI benchmark, prompted in the style of ASCII art.
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners
Large language models (LLMs) exhibit a wide range of promising capabilities -- from step-by-step planning to commonsense reasoning -- that may provide utility for robots, but remain prone to confidently hallucinated predictions.
Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation.
Language to Rewards for Robotic Skill Synthesis
However, since low-level robot actions are hardware-dependent and underrepresented in LLM training corpora, existing efforts in applying LLMs to robotics have largely treated LLMs as semantic planners or relied on human-engineered control primitives to interface with the robot.
Sonicverse: A Multisensory Simulation Platform for Embodied Household Agents that See and Hear
We introduce Sonicverse, a multisensory simulation platform with integrated audio-visual simulation for training household agents that can both see and hear.
IMBUE: In-Memory Boolean-to-CUrrent Inference ArchitecturE for Tsetlin Machines
Non-volatile memory devices such as Resistive RAM (ReRAM) offer integrated switching and storage capabilities showing promising performance for ML applications.
Large Language Models Need Holistically Thought in Medical Conversational QA
The medical conversational question answering (CQA) system aims at providing a series of professional medical services to improve the efficiency of medical care.
Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks
Our work highlights the potential of seamlessly unifying explicit rule learning via CoNNs and implicit pattern learning in LMs, paving the way for true symbolic comprehension capabilities.
PaLM-E: An Embodied Multimodal Language Model
Large language models excel at a wide range of complex tasks.
Ranked #2 on
Visual Question Answering (VQA)
on OK-VQA
Open-World Object Manipulation using Pre-trained Vision-Language Models
This brings up a notably difficult challenge for robots: while robot learning approaches allow robots to learn many different behaviors from first-hand experience, it is impractical for robots to have first-hand experiences that span all of this semantic information.
Grounded Decoding: Guiding Text Generation with Grounded Models for Embodied Agents
Recent progress in large language models (LLMs) has demonstrated the ability to learn and leverage Internet-scale knowledge through pre-training with autoregressive models.
Scaling Robot Learning with Semantically Imagined Experience
Specifically, we make use of the state of the art text-to-image diffusion models and perform aggressive data augmentation on top of our existing robotic manipulation datasets via inpainting various unseen objects for manipulation, backgrounds, and distractors with text guidance.
Large Language Models are Better Reasoners with Self-Verification
By performing a backward verification of the answers that LLM deduced for itself, we can obtain interpretable answer validation scores to select the candidate answer with the highest score.
RT-1: Robotics Transformer for Real-World Control at Scale
By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance.
A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations
Object-goal navigation (Object-nav) entails searching, recognizing and navigating to a target object.
Robotic Table Wiping via Reinforcement Learning and Whole-body Trajectory Optimization
This problem is challenging, as it requires planning wiping actions while reasoning over uncertain latent dynamics of crumbs and spills captured via high-dimensional visual observations.
SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds
3D object detection in point clouds is a core component for modern robotics and autonomous driving systems.
Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation
Despite decades of research, existing navigation systems still face real-world challenges when deployed in the wild, e. g., in cluttered home environments or in human-occupied public spaces.
Open-vocabulary Queryable Scene Representations for Real World Planning
Large language models (LLMs) have unlocked new capabilities of task planning from human instructions.
6D Camera Relocalization in Visually Ambiguous Extreme Environments
To this end, we propose: (i) a hierarchical localization system, where we leverage temporal information and (ii) a novel environment-aware image enhancement method to boost the robustness and accuracy.
Inner Monologue: Embodied Reasoning through Planning with Language Models
We investigate a variety of sources of feedback, such as success detection, scene description, and human interaction.
BEHAVIOR in Habitat 2.0: Simulator-Independent Logical Task Description for Benchmarking Embodied AI Agents
Robots excel in performing repetitive and precision-sensitive tasks in controlled environments such as warehouses and factories, but have not been yet extended to embodied AI agents providing assistance in household tasks.
LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs
The medical conversational system can relieve the burden of doctors and improve the efficiency of healthcare, especially during the pandemic.
Towards Better Chinese-centric Neural Machine Translation for Low-resource Languages
The last decade has witnessed enormous improvements in science and technology, stimulating the growing demand for economic and cultural exchanges in various countries.
Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment.
Multi-Robot Active Mapping via Neural Bipartite Graph Matching
Previous approaches either choose the frontier as the goal position via a myopic solution that hinders the time efficiency, or maximize the long-term value via reinforcement learning to directly regress the goal position, but does not guarantee the complete map construction.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
We explore how generating a chain of thought -- a series of intermediate reasoning steps -- significantly improves the ability of large language models to perform complex reasoning.
Ranked #36 on
Common Sense Reasoning
on CommonsenseQA
ADBCMM : Acronym Disambiguation by Building Counterfactuals and Multilingual Mixing
To address the above issue, this paper proposes an new method for acronym disambiguation, named as ADBCMM, which can significantly improve the performance of low-resource languages by building counterfactuals and multilingual mixing.
PSG: Prompt-based Sequence Generation for Acronym Extraction
In this paper, we propose a Prompt-based Sequence Generation (PSG) method for the acronym extraction task.
SimCLAD: A Simple Framework for Contrastive Learning of Acronym Disambiguation
In this paper, we propose a Simple framework for Contrastive Learning of Acronym Disambiguation (SimCLAD) method to better understand the acronym meanings.
Extracting and Inferring Personal Attributes from Dialogue
Personal attributes represent structured information about a person, such as their hobbies, pets, family, likes and dislikes.
Auto-Split: A General Framework of Collaborative Edge-Cloud AI
At the same time, large amounts of input data are collected at the edge of cloud.
iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks
We evaluate the new capabilities of iGibson 2. 0 to enable robot learning of novel tasks, in the hope of demonstrating the potential of this new simulator to support new research in embodied AI.
BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments
We introduce BEHAVIOR, a benchmark for embodied AI with 100 activities in simulation, spanning a range of everyday household chores such as cleaning, maintenance, and food preparation.
A Masked Segmental Language Model for Unsupervised Natural Language Segmentation
Segmentation remains an important preprocessing step both in languages where "words" or other important syntactic/semantic units (like morphemes) are not clearly delineated by white space, as well as when dealing with continuous speech data, where there is often no meaningful pause between words.
QoS-Aware Power Minimization of Distributed Many-Core Servers using Transfer Q-Learning
Then, it is coupled with vertical scaling using transfer Q-learning, which further tunes power/performance based on workload profile using dynamic voltage/frequency scaling (DVFS).
Towards Accurate Active Camera Localization
These approaches localize the camera in the discrete pose space and are agnostic to the localization-driven scene property, which restricts the camera pose accuracy in the coarse scale.
IGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes
We present iGibson 1. 0, a novel simulation environment to develop robotic solutions for interactive tasks in large-scale realistic scenes.
Summarizing Medical Conversations via Identifying Important Utterances
For the particular dataset used in this study, we show that high-quality summaries can be generated by extracting two types of utterances, namely, problem statements and treatment recommendations.
Joint Chinese Word Segmentation and Part-of-speech Tagging via Multi-channel Attention of Character N-grams
However, their work on modeling such contextual features is limited to concatenating the features or their embeddings directly with the input embeddings without distinguishing whether the contextual features are important for the joint task in the specific context.
NLPStatTest: A Toolkit for Comparing NLP System Performance
The toolkit provides a convenient and systematic way to compare NLP system performance that goes beyond statistical significance testing
Improving Biomedical Named Entity Recognition with Syntactic Information
The experimental results on six English benchmark datasets demonstrate that auto-processed syntactic information can be a useful resource for BioNER and our method with KVMN can appropriately leverage such information to improve model performance.
Ranked #1 on
Named Entity Recognition (NER)
on Species-800
Improving Constituency Parsing with Span Attention
Constituency parsing is a fundamental and important task for natural language understanding, where a good representation of contextual information can help this task.
Ranked #1 on
Constituency Parsing
on ATB
Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks
Specifically, we build the graph from chunks (n-grams) extracted from a lexicon and apply attention over the graph, so that different word pairs from the contexts within and across chunks are weighted in the model and facilitate the supertagging accordingly.
Ranked #2 on
CCG Supertagging
on CCGbank
ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation
To validate our method, we apply ReLMoGen to two types of tasks: 1) Interactive Navigation tasks, navigation problems where interactions with the environment are required to reach the destination, and 2) Mobile Manipulation tasks, manipulation tasks that require moving the robot base.
Improving Chinese Word Segmentation with Wordhood Memory Networks
Contextual features always play an important role in Chinese word segmentation (CWS).
Ranked #1 on
Chinese Word Segmentation
on CITYU
Studying Challenges in Medical Conversation with Structured Annotation
Medical conversation is a central part of medical care.
Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge
Chinese word segmentation (CWS) and part-of-speech (POS) tagging are important fundamental tasks for Chinese language processing, where joint learning of them is an effective one-step solution for both tasks.
Interactive Gibson Benchmark (iGibson 0.5): A Benchmark for Interactive Navigation in Cluttered Environments
We present Interactive Gibson Benchmark, the first comprehensive benchmark for training and evaluating Interactive Navigation: robot navigation strategies where physical interaction with objects is allowed and even encouraged to accomplish a task.
HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators
Different from other HRL solutions, HRL4IN handles the heterogeneous nature of the Interactive Navigation task by creating subgoals in different spaces in different phases of the task.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Neural Network Design for Energy-Autonomous AI Applications using Temporal Encoding
We design a PWM-based perceptron which can serve as the fundamental building block for NNs, by using an entirely new method of realising arithmetic in the PWM domain.
WTMED at MEDIQA 2019: A Hybrid Approach to Biomedical Natural Language Inference
Natural language inference (NLI) is challenging, especially when it is applied to technical domains such as biomedical settings.
ChiMed: A Chinese Medical Corpus for Question Answering
Question answering (QA) is a challenging task in natural language processing (NLP), especially when it is applied to specific domains.
PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking
In this work, we formulate the 6D object pose tracking problem in the Rao-Blackwellized particle filtering framework, where the 3D rotation and the 3D translation of an object are decoupled.
A Behavioral Approach to Visual Navigation with Graph Localization Networks
Inspired by research in psychology, we introduce a behavioral approach for visual navigation using topological maps.
Composite Shape Modeling via Latent Space Factorization
We present a novel neural network architecture, termed Decomposer-Composer, for semantic structure-aware 3D shape modeling.
Gibson Env: Real-World Perception for Embodied Agents
Developing visual perception models for active agents and sensorimotor control are cumbersome to be done in the physical world, as existing algorithms are too slow to efficiently learn in real-time and robots are fragile and costly.
Coding Structures and Actions with the COSTA Scheme in Medical Conversations
This paper describes the COSTA scheme for coding structures and actions in conversation.
VUNet: Dynamic Scene View Synthesis for Traversability Estimation using an RGB Camera
We present VUNet, a novel view(VU) synthesis method for mobile robots in dynamic environments, and its application to the estimation of future traversability.
NeuralFDR: Learning Discovery Thresholds from Hypothesis Features
For example, in genetic association studies, each hypothesis tests the correlation between a variant and the trait.
CROWD-IN-THE-LOOP: A Hybrid Approach for Annotating Semantic Roles
Crowdsourcing has proven to be an effective method for generating labeled data for a range of NLP tasks.
Learning Word Representations with Regularization from Prior Knowledge
This paper presents a unified framework that leverages pre-learned or external priors, in the form of a regularizer, for enhancing conventional language model-based embedding learning.
Capturing divergence in dependency trees to improve syntactic projection
These patterns can then be used to improve structural projection algorithms, allowing for better performing NLP tools for resource-poor languages, in particular those that may not have large amounts of annotated data necessary for traditional, fully-supervised methods.
Annotating and Detecting Medical Events in Clinical Notes
Early detection and treatment of diseases that onset after a patient is admitted to a hospital, such as pneumonia, is critical to improving and reducing costs in healthcare.
Annotating Clinical Events in Text Snippets for Phenotype Detection
Early detection and treatment of diseases that onset after a patient is admitted to a hospital, such as pneumonia, is critical to improving and reducing costs in healthcare.
Enriching ODIN
In this paper, we describe the expansion of the ODIN resource, a database containing many thousands of instances of Interlinear Glossed Text (IGT) for over a thousand languages harvested from scholarly linguistic papers posted to the Web.
Modern Chinese Helps Archaic Chinese Processing: Finding and Exploiting the Shared Properties
Languages change over time and ancient languages have been studied in linguistics and other related fields.
Discriminative Relational Topic Models
Many scientific and engineering fields involve analyzing network data.
Measuring the Divergence of Dependency Structures Cross-Linguistically to Improve Syntactic Projection Algorithms
Syntactic parses can provide valuable information for many NLP tasks, such as machine translation, semantic analysis, etc.
Statistical Section Segmentation in Free-Text Clinical Records
Automatically segmenting and classifying clinical free text into sections is an important first step to automatic information retrieval, information extraction and data mining tasks, as it helps to ground the significance of the text within.
Using a Goodness Measurement for Domain Adaptation: A Case Study on Chinese Word Segmentation
Domain adaptation is an important topic for natural language processing.
Effort of Genre Variation and Prediction of System Performance
Our experiments show that the predicted scores are close to the real scores when tested on the CTB data.
