Search Results for author:
Found 129 papers, 58 papers with code
Social Norms-Grounded Machine Ethics in Complex Narrative Situation
Besides a norm-grounding knowledge model, we present a novel norm-supported ethical judgment model in line with neural module networks to alleviate dilemma situations and improve norm-level explainability.
XGLUE: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and Generation
In this paper, we introduce XGLUE, a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora, and evaluate their performance across a diverse set of cross-lingual tasks.
Understand before Answer: Improve Temporal Reading Comprehension via Precise Question Understanding
Precise question understanding is critical for temporal reading comprehension.
Hypertext Entity Extraction in Webpage
Webpage entity extraction is a fundamental natural language processing task in both research and applications.
Instructed Language Models with Retrievers Are Powerful Entity Linkers
Generative approaches powered by large language models (LLMs) have demonstrated emergent abilities in tasks that require complex reasoning abilities.
Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency
We propose CoherentED, an ED system equipped with novel designs aimed at enhancing the coherence of entity predictions.
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation
RUEL is the first method that connects user browsing data with typical recommendation datasets and can be generalized to various recommendation scenarios and datasets.
Investigating the Learning Behaviour of In-context Learning: A Comparison with Supervised Learning
Large language models (LLMs) have shown remarkable capacity for in-context learning (ICL), where learning a new task from just a few training examples is done without being explicitly pre-trained.
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Moreover, our model even outperforms the largest closed LLMs, Anthropic's Claude and Google's Bard, on HumanEval and HumanEval+.
Ranked #3 on
Code Generation
on CodeContests
(Test Set pass@1 metric)
Allies: Prompting Large Language Model with Beam Search
With the advance of large language models (LLMs), the research field of LLM applications becomes more and more popular and the idea of constructing pipelines to accomplish complex tasks by stacking LLM API calls come true.
Synergistic Interplay between Search and Large Language Models for Information Retrieval
Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to modern retrieval models (RMs).
Alleviating Over-smoothing for Unsupervised Sentence Representation
Currently, learning better unsupervised sentence representations is the pursuit of many natural language processing communities.
Code Execution with Pre-trained Language Models
Code execution is a fundamental aspect of programming language semantics that reflects the exact behavior of the code.
Augmented Large Language Models with Parametric Knowledge Guiding
We demonstrate that our PKG framework can enhance the performance of "black-box" LLMs on a range of domain knowledge-intensive tasks that require factual (+7. 9%), tabular (+11. 9%), medical (+3. 0%), and multimodal (+8. 1%) knowledge.
Large Language Models are Strong Zero-Shot Retriever
In this work, we propose a simple method that applies a large language model (LLM) to large-scale retrieval in zero-shot scenarios.
WizardLM: Empowering Large Language Models to Follow Complex Instructions
In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans.
Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval
To address this challenge, we propose ToRoDer (TypOs-aware bottlenecked pre-training for RObust DEnse Retrieval), a novel re-training strategy for DRs that increases their robustness to misspelled queries while preserving their effectiveness in downstream retrieval tasks.
Inference with Reference: Lossless Acceleration of Large Language Models
We propose LLMA, an LLM accelerator to losslessly speed up Large Language Model (LLM) inference with references.
Large Language Models are Diverse Role-Players for Summarization Evaluation
First, we propose to model objective and subjective dimensions of generated text based on roleplayers prompting mechanism.
Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval
In this paper, we propose to mine and generate self-supervised training data based on a large-scale unlabeled corpus.
Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval
However, we find that the performance of the cross-encoder re-ranker is heavily influenced by the number of training samples and the quality of negative samples, which is hard to obtain in the cross-lingual setting.
Bridge the Gap between Language models and Tabular Understanding
For instance, TPLMs jointly pre-trained with table and text input could be effective for tasks also with table-text joint input like table question answering, but it may fail for tasks with only tables or text as input such as table retrieval.
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Retrieval
The conventional dense retrieval paradigm relies on encoding images and texts into dense representations using dual-stream encoders, however, it faces challenges with low retrieval speed in large-scale retrieval scenarios.
Modeling Sequential Sentence Relation to Improve Cross-lingual Dense Retrieval
Specifically, we propose a multilingual PLM called masked sentence model (MSM), which consists of a sentence encoder to generate the sentence representations, and a document encoder applied to a sequence of sentence vectors from a document.
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Sparse Retrieval
To address this issue, we propose a novel sparse retrieval paradigm for ITR that exploits sparse representations in the vocabulary space for images and texts.
Iterative Proposal Refinement for Weakly-Supervised Video Grounding
Weakly-Supervised Video Grounding (WSVG) aims to localize events of interest in untrimmed videos with only video-level annotations.
Adam: Dense Retrieval Distillation with Adaptive Dark Examples
Different from previous works that only rely on one positive and hard negatives as candidate passages, we create dark examples that all have moderate relevance to the query through mixing-up and masking in discrete space.
Fine-Grained Distillation for Long Document Retrieval
Long document retrieval aims to fetch query-relevant documents from a large-scale collection, where knowledge distillation has become de facto to improve a retriever by mimicking a heterogeneous yet powerful cross-encoder.
MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders are Better Dense Retrievers
Pre-trained Transformers (\eg BERT) have been commonly used in existing dense retrieval methods for parameter initialization, and recent studies are exploring more effective pre-training tasks for further improving the quality of dense vectors.
Text Embeddings by Weakly-Supervised Contrastive Pre-training
This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a wide range of tasks.
Ranked #11 on
Only Connect Walls Dataset Task 1 (Grouping)
on OCW
(using extra training data)
VATLM: Visual-Audio-Text Pre-Training with Unified Masked Prediction for Speech Representation Learning
Although speech is a simple and effective way for humans to communicate with the outside world, a more realistic speech interaction contains multimodal information, e. g., vision, text.
Soft-Labeled Contrastive Pre-training for Function-level Code Representation
In this paper, we present \textbf{SCodeR}, a \textbf{S}oft-labeled contrastive pre-training framework with two positive sample construction methods to learn functional-level \textbf{Code} \textbf{R}epresentation.
Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA
However, training an effective dense table-text retriever is difficult due to the challenges of table-text discrepancy and data sparsity problem.
PROD: Progressive Distillation for Dense Retrieval
It is common that a better teacher model results in a bad student via distillation due to the nonnegligible gap between teacher and student.
LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval
In large-scale retrieval, the lexicon-weighting paradigm, learning weighted sparse representations in vocabulary space, has shown promising results with high quality and low latency.
LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrieval
The alignment is achieved by weakened knowledge distillations to enlighten the retriever via two aspects -- 1) a lexicon-augmented contrastive objective to challenge the dense encoder and 2) a pair-wise rank-consistent regularization to make dense model's behavior incline to the other.
SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval
It employs a simple bottleneck architecture that learns to compress the passage information into a dense vector through self-supervised pre-training.
Rethinking the Value of Gazetteer in Chinese Named Entity Recognition
Gazetteer is widely used in Chinese named entity recognition (NER) to enhance span boundary detection and type classification.
Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation
This problem is further exacerbated when using DSI for cross-lingual retrieval, where document text and query text are in different languages.
KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP
This paper focuses on the data augmentation for low-resource NLP tasks where the training set is limited.
Towards Robust Ranker for Text Retrieval
A ranker plays an indispensable role in the de facto 'retrieval & rerank' pipeline, but its training still lags behind -- learning from moderate negatives or/and serving as an auxiliary module for a retriever.
Unsupervised Context Aware Sentence Representation Pretraining for Multi-lingual Dense Retrieval
On the multilingual sentence retrieval task Tatoeba, our model achieves new SOTA results among methods without using bilingual data.
Task-Specific Expert Pruning for Sparse Mixture-of-Experts
The sparse Mixture-of-Experts (MoE) model is powerful for large-scale pre-training and has achieved promising results due to its model capacity.
THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption
As more and more pre-trained language models adopt on-cloud deployment, the privacy issues grow quickly, mainly for the exposure of plain-text user data (e. g., search history, medical record, bank account).
Negative Sampling for Contrastive Representation Learning: A Review
The learn-to-compare paradigm of contrastive representation learning (CRL), which compares positive samples with negative ones for representation learning, has achieved great success in a wide range of domains, including natural language processing, computer vision, information retrieval and graph learning.
UnifieR: A Unified Retriever for Large-Scale Retrieval
Large-scale retrieval is to recall relevant documents from a huge collection given a query.
Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding
Despite the great success of spoken language understanding (SLU) in high-resource languages, it remains challenging in low-resource languages mainly due to the lack of labeled training data.
Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting
(2) How to cohere with context and preserve the knowledge when generating a stylized response.
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling
Large-scale cross-lingual pre-trained language models (xPLMs) have shown effectiveness in cross-lingual sequence labeling tasks (xSL), such as cross-lingual machine reading comprehension (xMRC) by transferring knowledge from a high-resource language to low-resource languages.
Transformer-Empowered Content-Aware Collaborative Filtering
Knowledge graph (KG) based Collaborative Filtering is an effective approach to personalizing recommendation systems for relatively static domains such as movies and books, by leveraging structured information from KG to enrich both item and user representations.
FORCE: A Framework of Rule-Based Conversational Recommender System
The conversational recommender systems (CRSs) have received extensive attention in recent years.
TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge
To address the problem, we propose augmenting TExt Generation via Task-specific and Open-world Knowledge (TegTok) in a unified framework.
HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations
To address these challenges, we present HeterMPC, a heterogeneous graph-based neural network for response generation in MPCs which models the semantics of utterances and interlocutors simultaneously with two types of nodes in a graph.
Multi-View Document Representation Learning for Open-Domain Dense Retrieval
Second, to prevent multi-view embeddings from collapsing to the same one, we further propose a global-local loss with annealed temperature to encourage the multiple viewers to better align with different potential queries.
ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification
Generating new events given context with correlated ones plays a crucial role in many event-centric reasoning tasks.
PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks
This paper focuses on the Data Augmentation for low-resource Natural Language Understanding (NLU) tasks.
NÜWA-LIP: Language Guided Image Inpainting with Defect-free VQGAN
Language guided image inpainting aims to fill in the defective regions of an image under the guidance of text while keeping non-defective regions unchanged.
PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings
A straightforward solution is resorting to more diverse positives from a multi-augmenting strategy, while an open question remains about how to unsupervisedly learn from the diverse positives but with uneven augmenting qualities in the text field.
CodeRetriever: Unimodal and Bimodal Contrastive Learning for Code Search
For bimodal contrastive learning, we leverage the documentation and in-line comments of code to build code-text pairs.
From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension
Cross-lingual Machine Reading Comprehension (xMRC) is challenging due to the lack of training data in low-resource languages.
NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion
To cover language, image, and video at the same time for different scenarios, a 3D transformer encoder-decoder framework is designed, which can not only deal with videos as 3D data but also adapt to texts and images as 1D and 2D data, respectively.
Ranked #1 on
Text-to-Video Generation
on Kinetics
Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning
The sequence representation plays a key role in the learning of matching degree between the dialogue context and the response.
Multimodal Dialogue Response Generation
In such a low-resource setting, we devise a novel conversational agent, Divter, in order to isolate parameters that depend on multimodal dialogues from the entire generation model.
RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models
Furthermore, we propose to evaluate the CRS models in an end-to-end manner, which can reflect the overall performance of the entire system rather than the performance of individual modules, compared to the separate evaluations of the two modules used in previous work.
EventBERT: A Pre-Trained Model for Event Correlation Reasoning
Event correlation reasoning infers whether a natural language paragraph containing multiple events conforms to human common sense.
Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning
For this task, the adoption of pre-trained language models (such as BERT) has led to remarkable progress in a number of benchmarks.
Learning Neural Templates for Recommender Dialogue System
Second, only the items mentioned in the training corpus have a chance to be recommended in the conversation.
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations
We study the problem of coarse-grained response selection in retrieval-based dialogue systems.
Learning to Ground Visual Objects for Visual Dialog
Specifically, a posterior distribution over visual objects is inferred from both context (history and questions) and answers, and it ensures the appropriate grounding of visual objects during the training process.
Re-entry Prediction for Online Conversations via Self-Supervised Learning
In recent years, world business in online discussions and opinion sharing on social media is booming.
Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding
Although various data augmentation approaches have been proposed to synthesize training data in low-resource target languages, the augmented data sets are often noisy, and thus impede the performance of SLU models.
Smart Bird: Learnable Sparse Attention for Efficient and Effective Transformer
We then sample token pairs based on their probability scores derived from the sketched attention matrix to generate different sparse attention index matrices for different attention heads.
Reasoning over Entity-Action-Location Graph for Procedural Text Understanding
Procedural text understanding aims at tracking the states (e. g., create, move, destroy) and locations of the entities mentioned in a given paragraph.
Neural Rule-Execution Tracking Machine For Transformer-Based Text Generation
Sequence-to-Sequence (S2S) neural text generation models, especially the pre-trained ones (e. g., BART and T5), have exhibited compelling performance on various natural language generation tasks.
Language Scaling for Universal Suggested Replies Model
Faced with increased compute requirements and low resources for language expansion, we build a single universal model for improving the quality and reducing run-time costs of our production system.
MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding
Recently, various neural models for multi-party conversation (MPC) have achieved impressive improvements on a variety of tasks such as addressee recognition, speaker identification and response prediction.
Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition
Named entity recognition (NER) is a fundamental component in many applications, such as Web Search and Voice Assistants.
Improving Zero-Shot Cross-lingual Transfer for Multilingual Question Answering over Knowledge Graph
That is, we can only access training data in a high-resource language, while need to answer multilingual questions without any labeled data in target languages.
Maria: A Visual Experience Powered Conversational Agent
The retriever aims to retrieve a correlated image to the dialog from an image index, while the visual concept detector extracts rich visual knowledge from the image.
CoSQA: 20,000+ Web Queries for Code Search and Question Answering
Finding codes given natural language query isb eneficial to the productivity of software developers.
Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text
Logical reasoning of text requires understanding critical logical information in the text and performing inference over them.
Ranked #7 on
Reading Comprehension
on ReClor
ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation
ProphetNet is a pre-training based natural language generation method which shows powerful performance on English text summarization and question generation tasks.
Integrating Pre-trained Model into Rule-based Dialogue Management
Rule-based dialogue management is still the most popular solution for industrial task-oriented dialogue systems for their interpretablility.
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Benchmark datasets have a significant impact on accelerating research in programming language tasks.
Ranked #1 on
Cloze Test
on CodeXGLUE - CT-maxmin
ChemistryQA: A Complex Question Answering Dataset from Chemistry
We notice that some real-world QA tasks are more complex, which cannot be solved by end-to-end neural networks or translated to any kind of formal representations.
Syntax-Enhanced Pre-trained Model
We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa.
Reinforced Multi-Teacher Selection for Knowledge Distillation
When multiple teacher models are available in distillation, the state-of-the-art methods assign a fixed weight to a teacher model in the whole distillation.
GLGE: A New General Language Generation Evaluation Benchmark
Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP).
CalibreNet: Calibration Networks for Multilingual Sequence Labeling
To tackle the challenge of lack of training data in low-resource languages, we dedicatedly develop a novel unsupervised phrase boundary recovery pre-training task to enhance the multilingual boundary detection capability of CalibreNet.
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation
Then, we devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.
A Graph Representation of Semi-structured Data for Web Question Answering
The abundant semi-structured data on the Web, such as HTML-based tables and lists, provide commercial search engines a rich information source for question answering (QA).
Towards Interpretable Reasoning over Paragraph Effects in Situation
We focus on the task of reasoning over paragraph effects in situation, which requires a model to understand the cause and effect described in a background paragraph, and apply the knowledge to a novel situation.
Knowledge-Aware Procedural Text Understanding with Multi-Stage Training
In this work, we focus on the task of procedural text understanding, which aims to comprehend such documents and track entities' states and locations during a process.
No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension
The Natural Questions (NQ) benchmark set brings new challenges to Machine Reading Comprehension: the answers are not only at different levels of granularity (long and short), but also of richer types (including no-answer, yes/no, single-span and multi-span).
GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis
Specifically, a cascaded labeling module is developed to enhance the interchange between aspect terms and improve the attention of sentiment tokens when labeling sentiment polarities.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+4
Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation
In a multi-turn knowledge-grounded dialog, the difference between the knowledge selected at different turns usually provides potential clues to knowledge selection, which has been largely neglected in previous research.
GraphCodeBERT: Pre-training Code Representations with Data Flow
Instead of taking syntactic-level structure of code like abstract syntax tree (AST), we use data flow in the pre-training stage, which is a semantic-level structure of code that encodes the relation of "where-the-value-comes-from" between variables.
Ranked #3 on
Type prediction
on ManyTypes4TypeScript
Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues
To address these issues, in this paper, we propose learning a context-response matching model with auxiliary self-supervised tasks designed for the dialogue data based on pre-trained language models.
Ranked #4 on
Conversational Response Selection
on E-commerce
Table2Charts: Recommending Charts by Learning Shared Table Representations
It is common for people to create different types of charts to explore a multi-dimensional dataset (table).
Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder
Generating inferential texts about an event in different perspectives requires reasoning over different contexts that the event occurs.
Ranked #1 on
Common Sense Reasoning
on Event2Mind test
(BLEU metric)
Mining Implicit Relevance Feedback from User Behavior for Web Question Answering
In this paper, we make the first study to explore the correlation between user behavior and passage relevance, and propose a novel approach for mining training data for Web QA.
Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning
In this paper, we formalize the music-conditioned dance generation as a sequence-to-sequence learning problem and devise a novel seq2seq architecture to efficiently process long sequences of music features and capture the fine-grained correspondence between music and dance.
Ranked #1 on
Motion Synthesis
on BRACE
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).
RikiNet: Reading Wikipedia Pages for Natural Question Answering
The representations are then fed into the predictor to obtain the span of the short answer, the paragraph of the long answer, and the answer type in a cascaded manner.
Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension
Multilingual pre-trained models could leverage the training data from a rich source language (such as English) to improve performance on low resource languages.
Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection
We study the detection of propagandistic text fragments in news articles.
LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network
The graph is used to obtain graph-enhanced contextual representations of words in Transformer-based architecture.
Pre-training Text Representations as Meta Learning
In this work, we introduce a learning algorithm which directly optimizes model's ability to learn text representations for effective learning of downstream tasks.
Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation
Furthermore, we propose a simple and effective method to mine the keyphrases of interest in the news article and build a first large-scale keyphrase-aware news headline corpus, which contains over 180K aligned triples of $<$news article, headline, keyphrase$>$.
Inferential Text Generation with Multiple Knowledge Sources and Meta-Learning
In this work, we use multiple knowledge sources as fuels for the model.
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation
In this paper, we introduce XGLUE, a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora and evaluate their performance across a diverse set of cross-lingual tasks.
DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding
Recent studies on open-domain question answering have achieved prominent performance improvement using pre-trained language models such as BERT.
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
Results show that CodeBERT achieves state-of-the-art performance on both natural language code search and code documentation generation tasks.
Ranked #1 on
Code Documentation Generation
on CodeSearchNet - Go
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa.
Ranked #1 on
Entity Typing
on Open Entity
Model Compression with Two-stage Multi-teacher Knowledge Distillation for Web Question Answering System
The experiment results show that our method can significantly outperform the baseline methods and even achieve comparable results with the original teacher models, along with substantial speedup of model inference.
Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base
We consider the problem of conversational question answering over a large-scale knowledge base.
Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning
Neural semantic parsing has achieved impressive results in recent years, yet its success relies on the availability of large amounts of supervised data.
Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering
In this work, we propose to automatically extract evidence from heterogeneous knowledge sources, and answer questions based on the extracted evidence.
Ranked #13 on
Common Sense Reasoning
on CommonsenseQA
Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering
These two problems lead to a poorly-trained semantic parsing model.
Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks
On XNLI, 1. 8% averaged accuracy improvement (on 15 languages) is obtained.
Cross-Lingual Natural Language Inference
Cross-Lingual Question Answering
+1
Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training
We propose Unicoder-VL, a universal encoder that aims to learn joint representations of vision and language in a pre-training manner.
Ranked #5 on
Image-to-Text Retrieval
on MS COCO
(Recall@10 metric)
Joint Type Inference on Entities and Relations via Graph Convolutional Networks
We develop a new paradigm for the task of joint entity relation extraction.
Ranked #1 on
Relation Extraction
on ACE 2005
(Sentence Encoder metric)
Model Compression with Multi-Task Knowledge Distillation for Web-scale Question Answering System
Deep pre-training and fine-tuning models (like BERT, OpenAI GPT) have demonstrated excellent results in question answering areas.
NeuronBlocks: Building Your NLP DNN Models Like Playing Lego
Deep Neural Networks (DNN) have been widely employed in industry to address various Natural Language Processing (NLP) tasks.
Assertion-based QA with Question-Aware Open Information Extraction
We present assertion based question answering (ABQA), an open domain question answering task that takes a question and a passage as inputs, and outputs a semi-structured assertion consisting of a subject, a predicate and a list of arguments.
