Search Results for author:
Found 118 papers, 63 papers with code
PALM: Pre-training an Autoencoding\&Autoregressive Language Model for Context-conditioned Generation
An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues.
Abstractive Text Summarization
Conversational Response Generation
+8
Alibaba Speech Translation Systems for IWSLT 2018
This work describes the En→De Alibaba speech translation system developed for the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2018.
A Unified Encoding of Structures in Transition Systems
Transition systems usually contain various dynamic structures (e. g., stacks, buffers).
S^4-Tuning: A Simple Cross-lingual Sub-network Tuning Method
The emergence of multilingual pre-trained language models makes it possible to adapt to target languages with only few labeled examples. However, vanilla fine-tuning tends to achieve degenerated and unstable results, owing to the Language Interference among different languages, and Parameter Overload under the few-sample transfer learning scenarios. To address two problems elegantly, we propose S^4-Tuning, a Simple Cross-lingual Sub-network Tuning method.
Word Reordering for Zero-shot Cross-lingual Structured Prediction
Adapting word order from one language to another is a key problem in cross-lingual structured prediction.
Rethinking Denoised Auto-Encoding in Language Pre-Training
Pre-trained self-supervised models such as BERT have achieved striking success in learning sequence representations, especially for natural language processing.
Bilingual Methods for Adaptive Training Data Selection for Machine Translation
In this paper, we propose a new data selection method which uses semi-supervised convolutional neural networks based on bitokens (Bi-SSCNNs) for training machine translation systems from a large bilingual corpus.
Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction
Multimodal named entity recognition and relation extraction (MNER and MRE) is a fundamental and crucial branch in information extraction.
Molecular Geometry-aware Transformer for accurate 3D Atomic System modeling
To alleviate this limitation, we propose Moleformer, a novel Transformer architecture that takes nodes (atoms) and edges (bonds and nonbonding atom pairs) as inputs and models the interactions among them using rotational and translational invariant geometry-aware spatial encoding.
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
In contrast to predominant paradigms of solely relying on sequence-to-sequence generation or encoder-based instance discrimination, mPLUG-2 introduces a multi-module composition network by sharing common universal modules for modality collaboration and disentangling different modality modules to deal with modality entanglement.
Ranked #1 on
Visual Grounding
on RefCOCO+ testA
Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning
To alleviate the above challenges, we exploit large language models (LLMs) as decomposers for effective table-based reasoning, which (i) decompose huge evidence (a huge table) into sub-evidence (a small table) to mitigate the interference of useless information for table reasoning; and (ii) decompose complex questions into simpler sub-questions for text reasoning.
One Model for All Domains: Collaborative Domain-Prefix Tuning for Cross-Domain NER
Previous typical solutions mainly obtain a NER model by pre-trained language models (PLMs) with data from a rich-resource domain and adapt it to the target domain.
Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing
Recently, the pre-trained text-to-text transformer model, namely T5, though not specialized for text-to-SQL parsing, has achieved state-of-the-art performance on standard benchmarks targeting domain generalization.
Ranked #1 on
Semantic Parsing
on spider
A Multi-Modal Geographic Pre-Training Method
Single-modal PTMs can barely make use of the important GC and therefore have limited performance.
Adaptively Clustering Neighbor Elements for Image Captioning
We design a novel global-local Transformer named \textbf{Ada-ClustFormer} (\textbf{ACF}) to generate captions.
Learning Trajectory-Word Alignments for Video-Language Tasks
Besides T2W attention, we also follow previous VDL-BERTs to set a word-to-patch (W2P) attention in the cross-modal encoder.
HiTeA: Hierarchical Temporal-Aware Video-Language Pre-training
We achieve state-of-the-art results on 15 well-established video-language understanding and generation tasks, especially on temporal-oriented datasets (e. g., SSv2-Template and SSv2-Label) with 8. 6% and 11. 1% improvement respectively.
Ranked #1 on
Zero-Shot Video Retrieval
on LSMDC
SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformers
We propose SeqDiffuSeq, a text diffusion model for sequence-to-sequence generation.
Reasoning with Language Model Prompting: A Survey
Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc.
HyPe: Better Pre-trained Language Model Fine-tuning with Hidden Representation Perturbation
Unlike previous works that only add noise to inputs or parameters, we argue that the hidden representations of Transformers layers convey more diverse and meaningful language information.
Chaining Simultaneous Thoughts for Numerical Reasoning
Given that rich information is hidden behind ubiquitous numbers in text, numerical reasoning over text should be an essential skill of AI systems.
Estimating Soft Labels for Out-of-Domain Intent Detection
Out-of-Domain (OOD) intent detection is important for practical dialog systems.
Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings
In this paper, we introduce the task of learning unsupervised dialogue embeddings.
Towards Generalizable and Robust Text-to-SQL Parsing
Text-to-SQL parsing tackles the problem of mapping natural language questions to executable SQL queries.
STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing
Concretely, we propose two novel pre-training objectives which respectively explore the context-dependent interactions of NL utterances and SQL queries within each text-to-SQL conversation: (i) schema state tracking (SST) objective that tracks and explores the schema states of context-dependent SQL queries in the form of schema-states by predicting and updating the value of each schema slot during interaction; (ii) utterance dependency tracking (UDT) objective that employs weighted contrastive learning to pull together two semantically similar NL utterances and push away the representations of semantically dissimilar NL utterances within each conversation.
Doc2Bot: Accessing Heterogeneous Documents via Conversational Bots
This paper introduces Doc2Bot, a novel dataset for building machines that help users seek information via conversations.
Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning
Multiple pre-training objectives fill the vacancy of the understanding capability of single-objective language modeling, which serves the ultimate purpose of pre-trained language models (PrLMs), generalizing well on a mass of scenarios.
EnTDA: Entity-to-Text based Data Augmentation Approach for Named Entity Recognition Tasks
To alleviate the excessive reliance on the dependency order among entities in existing augmentation paradigms, we develop an entity-to-text instead of text-to-entity based data augmentation method named: EnTDA to decouple the dependencies between entities by adding, deleting, replacing and swapping entities, and adopt these augmented data to bootstrap the generalization ability of the NER model.
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers
To improve the generalizability and stability of neural text-to-SQL parsers, we propose a model uncertainty constraint to refine the query representations by enforcing the output representations of different perturbed encoding networks to be consistent with each other.
SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation
To capture the structured dialog semantics, we pre-train the dialog understanding module via a novel tree-induced semi-supervised contrastive learning objective with the help of extra dialog annotations.
SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding
Pre-training methods with contrastive learning objectives have shown remarkable success in dialog understanding tasks.
A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions
In recent years, deep neural networks have significantly advanced this task by neural generation models, which automatically learn a mapping function from an input NL question to an output SQL query.
Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing
The importance of building text-to-SQL parsers which can be applied to new databases has long been acknowledged, and a critical step to achieve this goal is schema linking, i. e., properly recognizing mentions of unseen columns or tables when generating SQLs.
Adversarial Self-Attention for Language Understanding
Deep neural models (e. g. Transformer) naturally learn spurious features, which create a ``shortcut'' between the labels and inputs, thus impairing the generalization and robustness.
On the Learning of Non-Autoregressive Transformers
Non-autoregressive Transformer (NAT) is a family of text generation models, which aims to reduce the decoding latency by predicting the whole sentences in parallel.
Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systems
In this paper, we present Duplex Conversation, a multi-turn, multimodal spoken dialogue system that enables telephone-based agents to interact with customers like a human.
Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning
Specifically, vanilla prompt learning may struggle to utilize atypical instances by rote during fully-supervised training or overfit shallow patterns with low-shot data.
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
Large-scale pretrained foundation models have been an emerging paradigm for building artificial intelligence (AI) systems, which can be quickly adapted to a wide range of downstream tasks.
Ranked #1 on
Image Captioning
on COCO Captions
Directed Acyclic Transformer for Non-Autoregressive Machine Translation
Non-autoregressive Transformers (NATs) significantly reduce the decoding latency by generating all tokens in parallel.
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting
We study the knowledge extrapolation problem to embed new components (i. e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting.
Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction
To deal with these issues, we propose a novel Hierarchical Visual Prefix fusion NeTwork (HVPNeT) for visual-enhanced entity and relation extraction, aiming to achieve more effective and robust performance.
Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion
Since most MKGs are far from complete, extensive knowledge graph completion studies have been proposed focusing on the multimodal entity, relation extraction and link prediction.
Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning
Note that the previous parametric learning paradigm can be viewed as memorization regarding training data as a book and inference as the close-book test.
MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction
This paper presents MuCGEC, a multi-reference multi-source evaluation dataset for Chinese Grammatical Error Correction (CGEC), consisting of 7, 063 sentences collected from three Chinese-as-a-Second-Language (CSL) learner sources.
On Effectively Learning of Knowledge in Continual Pre-training
Pre-trained language models (PLMs) like BERT have made significant progress in various downstream NLP tasks.
Image Captioning In the Transformer Age
This drawback inspires the researchers to develop a homogeneous architecture that facilitates end-to-end training, for which Transformer is the perfect one that has proven its huge potential in both vision and language domains and thus can be used as the basic component of the visual encoder and language decoder in an IC pipeline.
Probing Structured Pruning on Multilingual Pre-trained Models: Settings, Algorithms, and Efficiency
Structured pruning has been extensively studied on monolingual pre-trained language models and is yet to be fully evaluated on their multilingual counterparts.
Parallel Instance Query Network for Named Entity Recognition
Each instance query predicts one entity, and by feeding all instance queries simultaneously, we can query all entities in parallel.
Ranked #1 on
Nested Named Entity Recognition
on GENIA
Chinese Named Entity Recognition
named-entity-recognition
+3
DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition
Our system wins 10 out of 13 tracks in the MultiCoNER shared task.
AISHELL-NER: Named Entity Recognition from Chinese Speech
Named Entity Recognition (NER) from speech is among Spoken Language Understanding (SLU) tasks, aiming to extract semantic information from the speech signal.
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base population.
Attribute Extraction
Cross-Domain Named Entity Recognition
+3
From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression
Unified in contrastive learning, CAP enables the pruned model to learn from the pre-trained model for task-agnostic knowledge, and fine-tuned model for task-specific knowledge.
ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition
As text representations take the most important role in MNER, in this paper, we propose {\bf I}mage-{\bf t}ext {\bf A}lignments (ITA) to align image features into the textual space, so that the attention mechanism in transformer-based pretrained textual embeddings can be better utilized.
Ranked #1 on
Multi-modal Named Entity Recognition
on Twitter-17
Multi-modal Named Entity Recognition
named-entity-recognition
LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-training
Previous works leverage logical forms to facilitate logical knowledge-conditioned text generation.
GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection
Pre-trained models have proved to be powerful in enhancing task-oriented dialog systems.
Ranked #1 on
End-To-End Dialogue Modelling
on MULTIWOZ 2.0
Achieving Human Parity on Visual Question Answering
The Visual Question Answering (VQA) task utilizes both visual image and language analysis to answer a textual question with respect to an image.
Ranked #11 on
Visual Question Answering
on VQA v2 test-dev
Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition
To fuse these heterogeneous factors, we propose a novel triaffine mechanism including triaffine attention and scoring.
Ranked #1 on
Nested Named Entity Recognition
on TAC-KBP 2017
Learning to Ask for Data-Efficient Event Argument Extraction
Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles.
DialogueCSE: Dialogue-based Contrastive Learning of Sentence Embeddings
Learning sentence embeddings from dialogues has drawn increasing attention due to its low annotation cost and high domain adaptability.
MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations
Entity retrieval, which aims at disambiguating mentions to canonical entities from massive KBs, is essential for many tasks in natural language processing.
Ranked #1 on
Entity Retrieval
on ZESHEL
Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning
Recent pretrained language models extend from millions to billions of parameters.
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting
Most NER methods rely on extensive labeled data for model training, which struggles in the low-resource scenarios with limited training data.
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
Large-scale pre-trained language models have contributed significantly to natural language processing by demonstrating remarkable abilities as few-shot learners.
Ranked #1 on
Few-Shot Learning
on SST-2 Binary classification
Product-oriented Machine Translation with Cross-modal Cross-lingual Pre-training
Firstly, there are many specialized jargons in the product description, which are ambiguous to translate without the product image.
Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialogue State Tracking
Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset.
Multi-View Cross-Lingual Structured Prediction with Minimum Supervision
In structured prediction problems, cross-lingual transfer learning is an efficient way to train quality models for low-resource languages, and further improvement can be obtained by learning from multiple source languages.
Risk Minimization for Zero-shot Sequence Labeling
In this paper, we propose a novel unified framework for zero-shot sequence labeling with minimum risk training and design a new decomposable risk function that models the relations between the predicted labels from the source models and the true labels.
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.
Ranked #1 on
Medical Relation Extraction
on CMeIE
Document-level Relation Extraction as Semantic Segmentation
Specifically, we leverage an encoder module to capture the context information of entities and a U-shaped segmentation module over the image-style feature map to capture global interdependency among triples.
Ranked #3 on
Relation Extraction
on ReDocRED
NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style Transfer
First, we observe that most words in the transferred sentence can be aligned with related words in the source sentence, so we explicitly model word alignments to suppress irrelevant words.
E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning
Vision-language pre-training (VLP) on large-scale image-text pairs has achieved huge success for the cross-modal downstream tasks.
A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents
Fine-grained opinion mining (OM) has achieved increasing attraction in the natural language processing (NLP) community, which aims to find the opinion structures of {``}Who expressed what opinions towards what{''} in one sentence.
Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialog State Tracking
Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset.
Ranked #1 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
(using extra training data)
A Semantic-based Method for Unsupervised Commonsense Question Answering
In this paper, we present a novel SEmantic-based Question Answering method (SEQA) for unsupervised commonsense question answering.
StructuralLM: Structural Pre-training for Form Understanding
Large pre-trained language models achieve state-of-the-art results when fine-tuned on downstream NLP tasks.
OntoED: Low-resource Event Detection with Ontology Embedding
Most of current methods to ED rely heavily on training instances, and almost ignore the correlation of event types.
Bridging the Domain Gap: Improve Informal Language Translation via Counterfactual Domain Adaptation
Despite the near-human performances already achieved on formal texts such as news articles, neural machine transla- tion still has difficulty in dealing with ”user-generated” texts that have diverse linguistic phenomena but lack large-scale high-quality parallel corpora.
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
We find empirically that the contextual representations computed on the retrieval-based input view, constructed through the concatenation of a sentence and its external contexts, can achieve significantly improved performance compared to the original input view based only on the sentence.
Ranked #1 on
Named Entity Recognition
on CMeEE
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion
Moreover, modeling and inferring complex relations of one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N) by previous knowledge graph completion approaches requires high model complexity and a large amount of training instances.
Improving Biomedical Pretrained Language Models with Knowledge
To this end, we propose KeBioLM, a biomedical pretrained language model that explicitly leverages knowledge from the UMLS knowledge bases.
Ranked #1 on
Named Entity Recognition
on JNLPBA
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
To this end, we focus on incorporating knowledge among relation labels into prompt-tuning for relation extraction and propose a Knowledge-aware Prompt-tuning approach with synergistic optimization (KnowPrompt).
Ranked #5 on
Dialog Relation Extraction
on DialogRE
(F1 (v1) metric)
Prototypical Representation Learning for Relation Extraction
This approach allows us to learn meaningful, interpretable prototypes for the final classification.
Photoproduction $γp \to K^+Λ(1520)$ in an effective Lagrangian approach
In addition to the $t$-channel $K$ and $K^\ast$ exchanges, the $u$-channel $\Lambda$ exchange, the $s$-channel nucleon exchange, and the interaction current, a minimal number of nucleon resonances in the $s$ channel are introduced in constructing the reaction amplitudes to describe the data.
High Energy Physics - Phenomenology Nuclear Theory
Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing
Semantic parsing has long been a fundamental problem in natural language processing.
Ranked #5 on
Dialogue State Tracking
on CoSQL
A Text GAN for Language Generation with Non-Autoregressive Generator
Despite the great success of Generative Adversarial Networks (GANs) in generating high-quality images, GANs for text generation still face two major challenges: first, most text GANs are unstable in training mainly due to ineffective optimization of the generator, and they heavily rely on maximum likelihood pretraining; second, most text GANs adopt autoregressive generators without latent variables, which largely limits the ability to learn latent representations for natural language text.
Nested Named Entity Recognition with Partially-Observed TreeCRFs
With the TreeCRF we achieve a uniform way to jointly model the observed and the latent nodes.
Ranked #11 on
Nested Named Entity Recognition
on ACE 2005
Bearings degradation monitoring indicators based on discarded projected space information and piecewise linear representation
To extract efficient indicators, in this paper we propose a method based on the discarded projected space information and piecewise linear representation (PLR) to build three bearings degradation monitoring indicators which are named SDHT2, VSDHT2 and NVSDHT2.
Fuzzy model identification based on mixture distribution analysis for bearings remaining useful life estimation using small training data set
In this work, we used the vibration signals data from a small number of bearings over an entire period of run-to-failure.
An Investigation of Potential Function Designs for Neural CRF
The neural linear-chain CRF model is one of the most widely-used approach to sequence labeling.
VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation
Existing work in multilingual pretraining has demonstrated the potential of cross-lingual transferability by training a unified Transformer encoder for multiple languages.
Keyphrase Extraction with Dynamic Graph Convolutional Networks and Diversified Inference
Keyphrase extraction (KE) aims to summarize a set of phrases that accurately express a concept or a topic covered in a given document.
Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor
The objective function of knowledge distillation is typically the cross-entropy between the teacher and the student's output distributions.
Automated Concatenation of Embeddings for Structured Prediction
Pretrained contextualized embeddings are powerful word representations for structured prediction tasks.
Ranked #1 on
Part-Of-Speech Tagging
on ARK
Aspect Based Sentiment Analysis with Aspect-Specific Opinion Spans
Such a design allows the model to extract aspect-specific opinion spans and then evaluate sentiment polarity by exploiting the extracted opinion features.
OpenUE: An Open Toolkit of Universal Extraction from Text
We introduce a prototype model and provide an open-source and extensible toolkit called OpenUE for various extraction tasks.
VECO: Variable Encoder-decoder Pre-training for Cross-lingual Understanding and Generation
Recent studies about learning multilingual representations have achieved significant performance gains across a wide range of downstream cross-lingual tasks.
AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network
The linear-chain Conditional Random Field (CRF) model is one of the most widely-used neural sequence labeling approaches.
Ranked #3 on
Chunking
on CoNLL 2003 (German)
More Embeddings, Better Sequence Labelers?
Recent work proposes a family of contextual embeddings that significantly improves the accuracy of sequence labelers over non-contextual embeddings.
Ranked #2 on
Chunking
on CoNLL 2003 (German)
Contrastive Triple Extraction with Generative Transformer
In this paper, we revisit the end-to-end triple extraction task for sequence generation.
Ranked #8 on
Relation Extraction
on WebNLG
FINDINGS OF THE IWSLT 2020 EVALUATION CAMPAIGN
The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2020) featured this year six challenge tracks: (i) Simultaneous speech translation, (ii) Video speech translation, (iii) Offline speech translation, (iv) Conversational speech translation, (v) Open domain translation, and (vi) Non-native speech translation.
A Joint Neural Model for Information Extraction with Global Features
OneIE performs end-to-end IE in four stages: (1) Encoding a given sentence as contextualized word representations; (2) Identifying entity mentions and event triggers as nodes; (3) Computing label scores for all nodes and their pairwise links using local classifiers; (4) Searching for the globally optimal graph with a beam decoder.
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation
An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues.
Ranked #1 on
Text Generation
on CNN/Daily Mail
Abstractive Text Summarization
Conversational Response Generation
+8
Structure-Level Knowledge Distillation For Multilingual Sequence Labeling
Multilingual sequence labeling is a task of predicting label sequences using a single unified model for multiple languages.
CoTK: An Open-Source Toolkit for Fast Development and Fair Evaluation of Text Generation
In text generation evaluation, many practical issues, such as inconsistent experimental settings and metric implementations, are often ignored but lead to unfair evaluation and untenable conclusions.
A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation
To further capture the causal and temporal dependencies between the sentences in a reasonable story, we employ multi-task learning which combines a discriminative objective to distinguish true and fake stories during fine-tuning.
Event Ticket Price Prediction with Deep Neural Network on Spatial-Temporal Sparse Data
However, given all the historical transaction records, it is challenging to predict the sale price of the remaining seats at any future timestamp, not only because that the sale price is relevant to a lot of features (seat locations, date-to-event of the transaction, event date, team performance, etc.
Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis
In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE).
Ranked #5 on
Aspect Sentiment Triplet Extraction
on SemEval
ARAML: A Stable Adversarial Training Framework for Text Generation
The generator is optimized with maximum likelihood estimation augmented by the discriminator's rewards instead of policy gradient.
Unsupervised Multi-modal Neural Machine Translation
Unsupervised neural machine translation (UNMT) has recently achieved remarkable results with only large monolingual corpora in each language.
Using Relevant Public Posts to Enhance News Article Summarization
A news article summary usually consists of 2-3 key sentences that reflect the gist of that news article.
