Search Results for author:
Found 115 papers, 62 papers with code
Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine Translation
Diverse NMT aims at generating multiple diverse yet faithful translations given a source sentence.
Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training
Most existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples.
A Multi-Agent Framework with Automated Decision Rule Optimization for Cross-Domain Misinformation Detection
To address these issues, we propose a MultiAgent Framework for cross-domain misinformation detection with Automated Decision Rule Optimization (MARO).
LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning
To deal with this issue, we propose a simple yet effective framework that dynamically improves the embedding model's representation learning for negative pairs based on their discriminative difficulty.
UniGenCoder: Merging Seq2Seq and Seq2Tree Paradigms for Unified Code Generation
Deep learning-based code generation has completely transformed the way developers write programs today.
Investigating Inference-time Scaling for Chain of Multi-modal Thought: A Preliminary Study
Recently, inference-time scaling of chain-of-thought (CoT) has been demonstrated as a promising approach for addressing multi-modal reasoning tasks.
Don't Get Lost in the Trees: Streamlining LLM Reasoning by Overcoming Tree Search Exploration Pitfalls
Recent advancements in tree search algorithms guided by verifiers have significantly enhanced the reasoning capabilities of large language models (LLMs), but at the cost of increased computational resources.
EpiCoder: Encompassing Diversity and Complexity in Code Generation
Effective instruction tuning is indispensable for optimizing code LLMs, aligning model behavior with user expectations and enhancing model performance in real-world applications.
"I've Heard of You!": Generate Spoken Named Entity Recognition Data for Unseen Entities
Spoken named entity recognition (NER) aims to identify named entities from speech, playing an important role in speech processing.
A Dual-Perspective Metaphor Detection Framework Using Large Language Models
Metaphor detection, a critical task in natural language processing, involves identifying whether a particular word in a sentence is used metaphorically.
Hard-Synth: Synthesizing Diverse Hard Samples for ASR using Zero-Shot TTS and LLM
Existing research primarily utilizes additional text data and predefined speech styles supported by TTS models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Exploring Optimal Transport-Based Multi-Grained Alignments for Text-Molecule Retrieval
In this work, we introduce the Optimal TRansport-based Multi-grained Alignments model (ORMA), a novel approach that facilitates multi-grained alignments between textual descriptions and molecules.
Ranked #2 on
Cross-Modal Retrieval
on ChEBI-20
Towards Cross-Modal Text-Molecule Retrieval with Better Modality Alignment
Cross-modal text-molecule retrieval model aims to learn a shared feature space of the text and molecule modalities for accurate similarity calculation, which facilitates the rapid screening of molecules with specific properties and activities in drug design.
Ranked #3 on
Cross-Modal Retrieval
on ChEBI-20
Not All Languages are Equal: Insights into Multilingual Retrieval-Augmented Generation
Experimental results reveal linguistic inequalities: 1) high-resource languages stand out in Monolingual Knowledge Extraction; 2) Indo-European languages lead RALMs to provide answers directly from documents, alleviating the challenge of expressing answers across languages; 3) English benefits from RALMs' selection bias and speaks louder in multilingual knowledge selection.
Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training
Diffusion-based text-to-image models have demonstrated impressive achievements in diversity and aesthetics but struggle to generate images with legible visual texts.
A Learning Rate Path Switching Training Paradigm for Version Updates of Large Language Models
Preliminary experiments demonstrate that PTFS achieves better pre-training performance, while CPT has lower training cost.
One2set + Large Language Model: Best Partners for Keyphrase Generation
Further analysis shows that: 1) the one2set paradigm owns the advantage of high recall, but suffers from improper assignments of supervision signals during training; 2) LLMs are powerful in keyphrase selection, but existing selection methods often make redundant selections.
Mitigating the Negative Impact of Over-association for Conversational Query Production
We carefully analyze the negative effects of this phenomenon on pretrained Seq2seq query producers and then propose effective instance-level weighting strategies for training to mitigate these issues from multiple perspectives.
AVG-LLaVA: A Large Multimodal Model with Adaptive Visual Granularity
Recently, when dealing with high-resolution images, dominant LMMs usually divide them into multiple local images and one global image, which will lead to a large number of visual tokens.
Revolutionizing Database Q&A with Large Language Models: Comprehensive Benchmark and Evaluation
However, there is still a lack of a comprehensive benchmark to evaluate the capabilities of different LLMs and their modular components in database QA.
Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation
Among them, the target text decoder is used to alleviate the language alignment burden, and the image tokenizer converts long sequences of pixels into shorter sequences of visual tokens, preventing the model from focusing on low-level visual features.
LiteSearch: Efficacious Tree Search for LLM
Recent research suggests that tree search algorithms (e. g. Monte Carlo Tree Search) can dramatically boost LLM performance on complex mathematical reasoning tasks.
Towards Better Graph-based Cross-document Relation Extraction via Non-bridge Entity Enhancement and Prediction Debiasing
However, these studies ignore the non-bridge entities, each of which co-occurs with only one target entity and offers the semantic association between target entities for relation prediction.
Efficient k-Nearest-Neighbor Machine Translation with Dynamic Retrieval
In this paper, we first conduct a preliminary study to reveal two key limitations of $k$NN-MT-AR: 1) the optimization gap leads to inaccurate estimation of $\lambda$ for determining $k$NN retrieval skipping, and 2) using a fixed threshold fails to accommodate the dynamic demands for $k$NN retrieval at different timesteps.
Retaining Key Information under High Compression Ratios: Query-Guided Compressor for LLMs
This decline can be attributed to the loss of key information during the compression process.
A Survey on Multi-modal Machine Translation: Tasks, Methods and Challenges
In recent years, multi-modal machine translation has attracted significant interest in both academia and industry due to its superior performance.
On the Information Redundancy in Non-Autoregressive Translation
By manually annotating the NAT outputs, we identify two types of information redundancy errors that correspond well to lexical and reordering multi-modality problems.
Self-Consistency Boosts Calibration for Math Reasoning
Calibration, which establishes the correlation between accuracy and model confidence, is important for LLM development.
Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal
When conducting continual learning based on a publicly-released LLM checkpoint, the availability of the original training data may be non-existent.
Fine-Grained Self-Endorsement Improves Factuality and Reasoning
Experiments on Biographies show that our method can effectively improve the factuality of generations with simple and intuitive prompts across different scales of LLMs.
TDAG: A Multi-Agent Framework based on Dynamic Task Decomposition and Agent Generation
The emergence of Large Language Models (LLMs) like ChatGPT has inspired the development of LLM-based agents capable of addressing complex, real-world tasks.
Using Large Language Model for End-to-End Chinese ASR and NER
In this work, we connect the Whisper encoder with ChatGLM3 and provide in-depth comparisons of these two approaches using Chinese automatic speech recognition (ASR) and name entity recognition (NER) tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Conditional Variational Autoencoder for Sign Language Translation with Cross-Modal Alignment
The first KL divergence optimizes the conditional variational autoencoder and regularizes the encoder outputs, while the second KL divergence performs a self-distillation from the posterior path to the prior path, ensuring the consistency of decoder outputs.
Response Enhanced Semi-supervised Dialogue Query Generation
Based on the observation that the search query is typically related to the topic of dialogue response, we train a response-augmented query producer (RA) to provide rich and effective training signals for QP.
Retrieval-augmented Multi-modal Chain-of-Thoughts Reasoning for Large Language Models
The advancement of Large Language Models (LLMs) has brought substantial attention to the Chain of Thought (CoT) approach, primarily due to its ability to enhance the capability of LLMs on complex reasoning tasks.
GENET: Unleashing the Power of Side Information for Recommendation via Hypergraph Pre-training
(2) The diverse semantics of side information that describes items and users from multi-level in a context different from recommendation systems.
IBADR: an Iterative Bias-Aware Dataset Refinement Framework for Debiasing NLU models
Then, we pair each sample with a bias indicator representing its bias degree, and use these extended samples to train a sample generator.
Towards Better Multi-modal Keyphrase Generation via Visual Entity Enhancement and Multi-granularity Image Noise Filtering
2) the input text and image are often not perfectly matched, and thus the image may introduce noise into the model.
Eva-KELLM: A New Benchmark for Evaluating Knowledge Editing of LLMs
In this paper, we propose Eva-KELLM, a new benchmark for evaluating knowledge editing of LLMs.
On the Cultural Gap in Text-to-Image Generation
Accordingly, we propose a novel multi-modal metric that considers object-text alignment to filter the fine-tuning data in the target culture, which is used to fine-tune a T2I model to improve cross-cultural generation.
A Simple yet Effective Self-Debiasing Framework for Transformer Models
In this way, the top-layer sentence representation will be trained to ignore the common biased features encoded by the low-layer sentence representation and focus on task-relevant unbiased features.
A Sequence-to-Sequence&Set Model for Text-to-Table Generation
Specifically, in addition to a text encoder encoding the input text, our model is equipped with a table header generator to first output a table header, i. e., the first row of the table, in the manner of sequence generation.
Exploring Better Text Image Translation with Multimodal Codebook
Text image translation (TIT) aims to translate the source texts embedded in the image to target translations, which has a wide range of applications and thus has important research value.
Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation
$k$-Nearest neighbor machine translation ($k$NN-MT) has attracted increasing attention due to its ability to non-parametrically adapt to new translation domains.
Revisiting Non-Autoregressive Translation at Scale
Experimental results on the large-scale WMT20 En-De show that the asymmetric architecture (e. g. bigger encoder and smaller decoder) can achieve comparable performance with the scaling model, while maintaining the superiority of decoding speed with standard NAT models.
BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation
We also introduce two deliberately designed test sets to verify the necessity of visual information: Ambiguous with the presence of ambiguous words, and Unambiguous in which the text context is self-contained for translation.
RC3: Regularized Contrastive Cross-lingual Cross-modal Pre-training
In this paper, we propose Regularized Contrastive Cross-lingual Cross-modal (RC^3) pre-training, which further exploits more abundant weakly-aligned multilingual image-text pairs.
From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey
In this paper, we comprehensively summarize representative studies from the perspectives of dominant models, datasets and evaluation metrics.
Search-Engine-augmented Dialogue Response Generation with Cheaply Supervised Query Production
We propose a dialogue model that can access the vast and dynamic information from any search engine for response generation.
A Multi-task Multi-stage Transitional Training Framework for Neural Chat Translation
To address these issues, in this paper, we propose a multi-task multi-stage transitional (MMT) training framework, where an NCT model is trained using the bilingual chat translation dataset and additional monolingual dialogues.
Towards Better Document-level Relation Extraction via Iterative Inference
Existing methods usually directly predict the relations of all entity pairs of input document in a one-pass manner, ignoring the fact that predictions of some entity pairs heavily depend on the predicted results of other pairs.
WR-ONE2SET: Towards Well-Calibrated Keyphrase Generation
Keyphrase generation aims to automatically generate short phrases summarizing an input document.
Getting the Most out of Simile Recognition
Simile recognition involves two subtasks: simile sentence classification that discriminates whether a sentence contains simile, and simile component extraction that locates the corresponding objects (i. e., tenors and vehicles).
Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis
Most existing pre-trained language representation models (PLMs) are sub-optimal in sentiment analysis tasks, as they capture the sentiment information from word-level while under-considering sentence-level information.
Towards Robust k-Nearest-Neighbor Machine Translation
Meanwhile we inject two types of perturbations into the retrieved pairs for robust training.
Type-Driven Multi-Turn Corrections for Grammatical Error Correction
First, they simply mix additionally-constructed training instances and original ones to train models, which fails to help models be explicitly aware of the procedure of gradual corrections.
A Variational Hierarchical Model for Neural Cross-Lingual Summarization
The goal of the cross-lingual summarization (CLS) is to convert a document in one language (e. g., English) to a summary in another one (e. g., Chinese).
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation
Most dominant neural machine translation (NMT) models are restricted to make predictions only according to the local context of preceding words in a left-to-right manner.
A Label Dependence-aware Sequence Generation Model for Multi-level Implicit Discourse Relation Recognition
Then, we employ a label sequence decoder to output the predicted labels in a top-down manner, where the predicted higher-level labels are directly used to guide the label prediction at the current level.
KGR^4: Retrieval, Retrospect, Refine and Rethink for Commonsense Generation
Generative commonsense reasoning requires machines to generate sentences describing an everyday scenario given several concepts, which has attracted much attention recently.
Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings
Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models.
Towards Making the Most of Dialogue Characteristics for Neural Chat Translation
Neural Chat Translation (NCT) aims to translate conversational text between speakers of different languages.
A Structure Self-Aware Model for Discourse Parsing on Multi-Party Dialogues
Conversational discourse structures aim to describe how a dialogue is organised, thus they are helpful for dialogue understanding and response generation.
Ranked #3 on
Discourse Parsing
on STAC
BACO: A Background Knowledge- and Content-Based Framework for Citing Sentence Generation
In this paper, we focus on the problem of citing sentence generation, which entails generating a short text to capture the salient information in a cited paper and the connection between the citing and cited paper.
Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation
A well-known limitation in pretrain-finetune paradigm lies in its inflexibility caused by the one-size-fits-all vocabulary.
Towards User-Driven Neural Machine Translation
Furthermore, we contribute the first Chinese-English parallel corpus annotated with user behavior called UDT-Corpus.
Exploring Dynamic Selection of Branch Expansion Orders for Code Generation
Due to the great potential in facilitating software development, code generation has attracted increasing attention recently.
Improving Tree-Structured Decoder Training for Code Generation via Mutual Learning
However, such a decoder only exploits the preorder traversal based preceding actions, which are insufficient to ensure correct action predictions.
Enhanced Aspect-Based Sentiment Analysis Models with Progressive Self-supervised Attention Learning
In aspect-based sentiment analysis (ABSA), many neural models are equipped with an attention mechanism to quantify the contribution of each context word to sentiment prediction.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Structural Information Preserving for Graph-to-Text Generation
The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs.
Ranked #14 on
Data-to-Text Generation
on WebNLG
Dynamic Context-guided Capsule Network for Multimodal Machine Translation
Particularly, we represent the input image with global and regional visual features, we introduce two parallel DCCNs to model multimodal context vectors with visual features at different granularities.
Ranked #3 on
Multimodal Machine Translation
on Multi30K
A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation
Multi-modal neural machine translation (NMT) aims to translate source sentences into a target language paired with images.
Modeling Discourse Structure for Document-level Neural Machine Translation
Finally, we combine the discourse structure information with the word embedding before it is fed into the encoder.
Neural Simile Recognition with Cyclic Multitask Learning and Local Attention
Simile recognition is to detect simile sentences and to extract simile components, i. e., tenors and vehicles.
Iterative Dual Domain Adaptation for Neural Machine Translation
Previous studies on the domain adaptation for neural machine translation (NMT) mainly focus on the one-pass transferring out-of-domain translation knowledge to in-domain NMT model.
Graph-based Neural Sentence Ordering
Sentence ordering is to restore the original paragraph from a set of sentences.
Grounding-Tracking-Integration
In this paper, we study Tracking by Language that localizes the target box sequence in a video based on a language query.
A Real-time Global Inference Network for One-stage Referring Expression Comprehension
Referring Expression Comprehension (REC) is an emerging research spot in computer vision, which refers to detecting the target region in an image given an text description.
Leveraging Dependency Forest for Neural Medical Relation Extraction
Medical relation extraction discovers relations between entity mentions in text, such as research articles.
Exploiting Temporal Relationships in Video Moment Localization with Natural Language
We address the problem of video moment localization with natural language, i. e. localizing a video segment described by a natural language sentence.
Neural Collective Entity Linking Based on Recurrent Random Walk Network Learning
However, most neural collective EL methods depend entirely upon neural networks to automatically model the semantic dependencies between different EL decisions, which lack of the guidance from external knowledge.
Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis
In aspect-level sentiment classification (ASC), it is prevalent to equip dominant neural models with attention mechanisms, for the sake of acquiring the importance of each context word on the given aspect.
Aspect-Based Sentiment Analysis (ABSA)
Sentiment Classification
Semantic Neural Machine Translation using AMR
It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation models.
Towards Linear Time Neural Machine Translation with Capsule Networks
In this study, we first investigate a novel capsule network with dynamic routing for linear time Neural Machine Translation (NMT), referred as \textsc{CapsNMT}.
Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated Recurrent Networks
Experiments on WMT14 translation tasks demonstrate that ATR-based neural machine translation can yield competitive performance on English- German and English-French language pairs in terms of both translation quality and speed.
Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination
Based on this intuition, in this paper, we devote to distinguishing and exploiting word-level domain contexts for multi-domain NMT.
Neural Machine Translation with Decoding History Enhanced Attention
Neural machine translation with source-side attention have achieved remarkable performance.
Otem&Utem: Over- and Under-Translation Evaluation Metric for NMT
Although neural machine translation(NMT) yields promising translation performance, it unfortunately suffers from over- and under-translation is- sues [Tu et al., 2016], of which studies have become research hotspots in NMT.
Deconvolution-Based Global Decoding for Neural Machine Translation
A great proportion of sequence-to-sequence (Seq2Seq) models for Neural Machine Translation (NMT) adopt Recurrent Neural Network (RNN) to generate translation word by word following a sequential order.
Ranked #9 on
Machine Translation
on IWSLT2015 English-Vietnamese
GroupCap: Group-Based Image Captioning With Structured Relevance and Diversity Constraints
In offline optimization, we adopt an end-to-end formulation, which jointly trains the visual tree parser, the structured relevance and diversity constraints, as well as the LSTM based captioning model.
Accelerating Neural Transformer via an Average Attention Network
To alleviate this issue, we propose an average attention network as an alternative to the self-attention network in the decoder of the neural Transformer.
Ranked #60 on
Machine Translation
on WMT2014 English-German
Attention Regularized Sequence-to-Sequence Learning for E2E NLG Challenge
This paper describes our system used for the end-to-end (E2E) natural language generation (NLG) challenge.
Ranked #8 on
Data-to-Text Generation
on E2E NLG Challenge
Variational Recurrent Neural Machine Translation
Partially inspired by successful applications of variational recurrent neural networks, we propose a novel variational recurrent neural machine translation (VRNMT) model in this paper.
Asynchronous Bidirectional Decoding for Neural Machine Translation
The dominant neural machine translation (NMT) models apply unified attentional encoder-decoder neural networks for translation.
Improving Implicit Discourse Relation Recognition with Discourse-specific Word Embeddings
We introduce a simple and effective method to learn discourse-specific word embeddings (DSWE) for implicit discourse relation recognition.
A GRU-Gated Attention Model for Neural Machine Translation
In this paper, we propose a novel GRU-gated attention model (GAtt) for NMT which enhances the degree of discrimination of context vectors by enabling source representations to be sensitive to the partial translation generated by the decoder.
Convolution-Enhanced Bilingual Recursive Neural Network for Bilingual Semantic Modeling
After that, we fully incorporate information of different linguistic units into a bilinear semantic similarity model.
Bilingual Autoencoders with Global Descriptors for Modeling Parallel Sentences
Parallel sentence representations are important for bilingual and cross-lingual tasks in natural language processing.
Lattice-Based Recurrent Neural Network Encoders for Neural Machine Translation
Neural machine translation (NMT) heavily relies on word-level modelling to learn semantic representations of input sentences.
Cseq2seq: Cyclic Sequence-to-Sequence Learning
The vanilla sequence-to-sequence learning (seq2seq) reads and encodes a source sequence into a fixed-length vector only once, suffering from its insufficiency in modeling structural correspondence between the source and target sequence.
BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings
In this paper, we propose a bidimensional attention based recursive autoencoder (BattRAE) to integrate clues and sourcetarget interactions at multiple levels of granularity into bilingual phrase representations.
Variational Neural Machine Translation
Models of neural machine translation are often from a discriminative family of encoderdecoders that learn a conditional distribution of a target sentence given a source sentence.
Variational Neural Discourse Relation Recognizer
In order to perform efficient inference and learning, we introduce neural discourse relation models to approximate the prior and posterior distributions of the latent variable, and employ these approximated distributions to optimize a reparameterized variational lower bound.
Neural Discourse Relation Recognition with Semantic Memory
Inspired by this, we propose a neural recognizer for implicit discourse relation analysis, which builds upon a semantic memory that stores knowledge in a distributed fashion.
