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Found 28 papers, 12 papers with code
Hierarchical Multi-label Text Classification with Horizontal and Vertical Category Correlations
Hierarchical multi-label text classification (HMTC) deals with the challenging task where an instance can be assigned to multiple hierarchically structured categories at the same time.
Multi Label Text Classification
Multi-Label Text Classification
+1
Mitigating Reversal Curse in Large Language Models via Semantic-aware Permutation Training
Accordingly, permutation on the training data is considered as a potential solution, since this can make the model predict antecedent words or tokens.
UniEdit: A Unified Tuning-Free Framework for Video Motion and Appearance Editing
Recent advances in text-guided video editing have showcased promising results in appearance editing (e. g., stylization).
Bi-directional Adapter for Multi-modal Tracking
To handle this problem, we propose a novel multi-modal visual prompt tracking model based on a universal bi-directional adapter, cross-prompting multiple modalities mutually.
Ranked #2 on
Rgb-T Tracking
on LasHeR
GAIA: Zero-shot Talking Avatar Generation
Zero-shot talking avatar generation aims at synthesizing natural talking videos from speech and a single portrait image.
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers
Large Language Models (LLMs) excel in various tasks, but they rely on carefully crafted prompts that often demand substantial human effort.
Retrosynthesis Prediction with Local Template Retrieval
Furthermore, we propose a lightweight adapter to adjust the weights when combing neural network and KNN predictions conditioned on the hidden representation and the retrieved templates.
Extract and Attend: Improving Entity Translation in Neural Machine Translation
Inspired by this translation process, we propose an Extract-and-Attend approach to enhance entity translation in NMT, where the translation candidates of source entities are first extracted from a dictionary and then attended to by the NMT model to generate the target sentence.
Deliberate then Generate: Enhanced Prompting Framework for Text Generation
Large language models (LLMs) have shown remarkable success across a wide range of natural language generation tasks, where proper prompt designs make great impacts.
ResiDual: Transformer with Dual Residual Connections
In this paper, we propose ResiDual, a novel Transformer architecture with Pre-Post-LN (PPLN), which fuses the connections in Post-LN and Pre-LN together and inherits their advantages while avoids their limitations.
A Study on ReLU and Softmax in Transformer
This paper sheds light on the following points: 1) Softmax and ReLU use different normalization methods over elements which lead to different variances of results, and ReLU is good at dealing with a large number of key-value slots; 2) FFN and key-value memory are equivalent, and thus the Transformer can be viewed as a memory network where FFNs and self-attention networks are both key-value memories.
N-Gram Nearest Neighbor Machine Translation
Nearest neighbor machine translation augments the Autoregressive Translation~(AT) with $k$-nearest-neighbor retrieval, by comparing the similarity between the token-level context representations of the target tokens in the query and the datastore.
Empowering Diffusion Models on the Embedding Space for Text Generation
Diffusion models have achieved state-of-the-art synthesis quality on both visual and audio tasks, and recent works further adapt them to textual data by diffusing on the embedding space.
VideoDubber: Machine Translation with Speech-Aware Length Control for Video Dubbing
In this paper, we propose a machine translation system tailored for the task of video dubbing, which directly considers the speech duration of each token in translation, to match the length of source and target speech.
Bridging Music and Text with Crowdsourced Music Comments: A Sequence-to-Sequence Framework for Thematic Music Comments Generation
We consider a novel task of automatically generating text descriptions of music.
A Study of Syntactic Multi-Modality in Non-Autoregressive Machine Translation
Furthermore, we take the best of both and design a new loss function to better handle the complicated syntactic multi-modality in real-world datasets.
BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis
Combining this novel perspective of two-stage synthesis with advanced generative models (i. e., the diffusion models), the proposed BinauralGrad is able to generate accurate and high-fidelity binaural audio samples.
Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation
The task of Grammatical Error Correction (GEC) has received remarkable attention with wide applications in Natural Language Processing (NLP) in recent years.
A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond
While NAR generation can significantly accelerate inference speed for machine translation, the speedup comes at the cost of sacrificed translation accuracy compared to its counterpart, autoregressive (AR) generation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+11
Task-Oriented Dialogue System as Natural Language Generation
In this paper, we propose to formulate the task-oriented dialogue system as the purely natural language generation task, so as to fully leverage the large-scale pre-trained models like GPT-2 and simplify complicated delexicalization prepossessing.
Adaptive Nearest Neighbor Machine Translation
On four benchmark machine translation datasets, we demonstrate that the proposed method is able to effectively filter out the noises in retrieval results and significantly outperforms the vanilla kNN-MT model.
Towards Variable-Length Textual Adversarial Attacks
In this way, our approach is able to more comprehensively find adversarial examples around the decision boundary and effectively conduct adversarial attacks.
Incorporating BERT into Parallel Sequence Decoding with Adapters
Our framework is based on a parallel sequence decoding algorithm named Mask-Predict considering the bi-directional and conditional independent nature of BERT, and can be adapted to traditional autoregressive decoding easily.
Jointly Masked Sequence-to-Sequence Model for Non-Autoregressive Neural Machine Translation
In this work, we introduce a jointly masked sequence-to-sequence model and explore its application on non-autoregressive neural machine translation{\textasciitilde}(NAT).
Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation
Non-autoregressive translation (NAT) models remove the dependence on previous target tokens and generate all target tokens in parallel, resulting in significant inference speedup but at the cost of inferior translation accuracy compared to autoregressive translation (AT) models.
Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input
Non-autoregressive translation (NAT) models, which remove the dependence on previous target tokens from the inputs of the decoder, achieve significantly inference speedup but at the cost of inferior accuracy compared to autoregressive translation (AT) models.
Asynchronous Stochastic Composition Optimization with Variance Reduction
Composition optimization has drawn a lot of attention in a wide variety of machine learning domains from risk management to reinforcement learning.
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective
In this paper, we take a matrix factorization perspective of network embedding, and incorporate structure, content and label information of the network simultaneously.
