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Found 32 papers, 18 papers with code
Entity and Evidence Guided Document-Level Relation Extraction
In this paper, we propose a novel framework E2GRE (Entity and Evidence Guided Relation Extraction) that jointly extracts relations and the underlying evidence sentences by using large pretrained language model (LM) as input encoder.
Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection
To instantiate this proposal, we design an adaptive rationale guidance network for fake news detection (ARG), in which SLMs selectively acquire insights on news analysis from the LLMs' rationales.
A Miniaturised Camera-based Multi-Modal Tactile Sensor
The human tactile system is composed of various types of mechanoreceptors, each able to perceive and process distinct information such as force, pressure, texture, etc.
Combating Online Misinformation Videos: Characterization, Detection, and Future Directions
With information consumption via online video streaming becoming increasingly popular, misinformation video poses a new threat to the health of the online information ecosystem.
Tokenization Consistency Matters for Generative Models on Extractive NLP Tasks
We show that, with consistent tokenization, the model performs better in both in-domain and out-of-domain datasets, with a notable average of +1. 7 F2 gain when a BART model is trained on SQuAD and evaluated on 8 QA datasets.
Challenges in Explanation Quality Evaluation
This approach assumes that explanations which reach higher proxy scores will also provide a greater benefit to human users.
Language Agnostic Multilingual Information Retrieval with Contrastive Learning
Multilingual information retrieval (IR) is challenging since annotated training data is costly to obtain in many languages.
SpanDrop: Simple and Effective Counterfactual Learning for Long Sequences
Distilling supervision signal from a long sequence to make predictions is a challenging task in machine learning, especially when not all elements in the input sequence contribute equally to the desired output.
Neural Generation Meets Real People: Building a Social, Informative Open-Domain Dialogue Agent
We present Chirpy Cardinal, an open-domain social chatbot.
DRAG: Dynamic Region-Aware GCN for Privacy-Leaking Image Detection
However, these methods have two limitations: 1) they neglect other important elements like scenes, textures, and objects beyond the capacity of pretrained object detectors; 2) the correlation among objects is fixed, but a fixed correlation is not appropriate for all the images.
Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs
These questions often involve three time-related challenges that previous work fail to adequately address: 1) questions often do not specify exact timestamps of interest (e. g., "Obama" instead of 2000); 2) subtle lexical differences in time relations (e. g., "before" vs "after"); 3) off-the-shelf temporal KG embeddings that previous work builds on ignore the temporal order of timestamps, which is crucial for answering temporal-order related questions.
Ranked #2 on
Question Answering
on CronQuestions
Trustworthy AI: From Principles to Practices
In this review, we provide AI practitioners with a comprehensive guide for building trustworthy AI systems.
Open Temporal Relation Extraction for Question Answering
Understanding the temporal relations among events in text is a critical aspect of reading comprehension, which can be evaluated in the form of temporal question answering (TQA).
VeniBot: Towards Autonomous Venipuncture with Automatic Puncture Area and Angle Regression from NIR Images
For the automation, we focus on the positioning part and propose a Dual-In-Dual-Out network based on two-step learning and two-task learning, which can achieve fully automatic regression of the suitable puncture area and angle from near-infrared(NIR) images.
VeniBot: Towards Autonomous Venipuncture with Semi-supervised Vein Segmentation from Ultrasound Images
In the modern medical care, venipuncture is an indispensable procedure for both diagnosis and treatment.
Conversational AI Systems for Social Good: Opportunities and Challenges
Conversational artificial intelligence (ConvAI) systems have attracted much academic and commercial attention recently, making significant progress on both fronts.
Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification
Recent work on aspect-level sentiment classification has demonstrated the efficacy of incorporating syntactic structures such as dependency trees with graph neural networks(GNN), but these approaches are usually vulnerable to parsing errors.
Answering Open-Domain Questions of Varying Reasoning Steps from Text
We develop a unified system to answer directly from text open-domain questions that may require a varying number of retrieval steps.
Ranked #10 on
Question Answering
on HotpotQA
Neural Generation Meets Real People: Towards Emotionally Engaging Mixed-Initiative Conversations
At the end of the competition, Chirpy Cardinal progressed to the finals with an average rating of 3. 6/5. 0, a median conversation duration of 2 minutes 16 seconds, and a 90th percentile duration of over 12 minutes.
Do Syntax Trees Help Pre-trained Transformers Extract Information?
Our empirical analysis demonstrates that these syntax-infused transformers obtain state-of-the-art results on SRL and relation extraction tasks.
Biomedical and Clinical English Model Packages in the Stanza Python NLP Library
We introduce biomedical and clinical English model packages for the Stanza Python NLP library.
Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations
We investigate the problem of generating informative questions in information-asymmetric conversations.
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages.
Answering Complex Open-domain Questions Through Iterative Query Generation
It is challenging for current one-step retrieve-and-read question answering (QA) systems to answer questions like "Which novel by the author of 'Armada' will be adapted as a feature film by Steven Spielberg?"
Ranked #51 on
Question Answering
on HotpotQA
Exploiting Multi-domain Visual Information for Fake News Detection
In the real world, fake-news images may have significantly different characteristics from real-news images at both physical and semantic levels, which can be clearly reflected in the frequency and pixel domain, respectively.
Universal Dependency Parsing from Scratch
This paper describes Stanford's system at the CoNLL 2018 UD Shared Task.
Ranked #4 on
Dependency Parsing
on Universal Dependencies
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
Dependency trees help relation extraction models capture long-range relations between words.
Ranked #6 on
Relation Extraction
on Re-TACRED
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers.
Ranked #36 on
Question Answering
on HotpotQA
Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context
We know very little about how neural language models (LM) use prior linguistic context.
Stanford's Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task
This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies.
Arc-swift: A Novel Transition System for Dependency Parsing
Transition-based dependency parsers often need sequences of local shift and reduce operations to produce certain attachments.
Building DNN Acoustic Models for Large Vocabulary Speech Recognition
We compare standard DNNs to convolutional networks, and present the first experiments using locally-connected, untied neural networks for acoustic modeling.
Ranked #11 on
Speech Recognition
on swb_hub_500 WER fullSWBCH
