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Found 17 papers, 8 papers with code
LayerConnect: Hypernetwork-Assisted Inter-Layer Connector to Enhance Parameter Efficiency
Pre-trained Language Models (PLMs) are the cornerstone of the modern Natural Language Processing (NLP).
QiuNiu: A Chinese Lyrics Generation System with Passage-Level Input
In this paper, we demonstrate the QiuNiu, a Chinese lyrics generation system which is conditioned on passage-level text rather than a few attributes or keywords.
Easy and Efficient Transformer: Scalable Inference Solution For Large NLP Model
To fill such a gap, we introduce a scalable inference solution: Easy and Efficient Transformer (EET), including a series of transformer inference optimization at the algorithm and implementation levels.
Tailoring Language Generation Models under Total Variation Distance
The standard paradigm of neural language generation adopts maximum likelihood estimation (MLE) as the optimizing method.
Facial Action Unit Detection and Intensity Estimation from Self-supervised Representation
As a fine-grained and local expression behavior measurement, facial action unit (FAU) analysis (e. g., detection and intensity estimation) has been documented for its time-consuming, labor-intensive, and error-prone annotation.
Generating Coherent Narratives by Learning Dynamic and Discrete Entity States with a Contrastive Framework
Despite advances in generating fluent texts, existing pretraining models tend to attach incoherent event sequences to involved entities when generating narratives such as stories and news.
Unraveling the Mystery of Artifacts in Machine Generated Text
As neural Text Generation Models (TGM) have become more and more capable of generating text indistinguishable from human-written ones, the misuse of text generation technologies can have serious ramifications.
Probing Simile Knowledge from Pre-trained Language Models
In this paper, we probe simile knowledge from PLMs to solve the SI and SG tasks in the unified framework of simile triple completion for the first time.
DecBERT: Enhancing the Language Understanding of BERT with Causal Attention Masks
In contrast, Transformer Decoder with the causal attention masks is naturally sensitive to the word order.
LaMemo: Language Modeling with Look-Ahead Memory
Although Transformers with fully connected self-attentions are powerful to model long-term dependencies, they are struggling to scale to long texts with thousands of words in language modeling.
I-Tuning: Tuning Frozen Language Models with Image for Lightweight Image Captioning
Different to these heavy-cost models, we introduce a lightweight image captioning framework (I-Tuning), which contains a small number of trainable parameters.
A Frustratingly Simple Approach for End-to-End Image Captioning
Before training the captioning models, an extra object detector is utilized to recognize the objects in the image at first.
Youling: an AI-Assisted Lyrics Creation System
In the lyrics generation process, \textit{Youling} supports traditional one pass full-text generation mode as well as an interactive generation mode, which allows users to select the satisfactory sentences from generated candidates conditioned on preceding context.
Unsupervised Domain Adaptation with Adapter
However, fine-tuning all the parameters of the PrLM on a small domain-specific corpus distort the learned generic knowledge, and it is also expensive to deployment a whole fine-tuned PrLM for each domain.
Stylized Dialogue Response Generation Using Stylized Unpaired Texts
However, this task is far from well-explored due to the difficulties of rendering a particular style in coherent responses, especially when the target style is embedded only in unpaired texts that cannot be directly used to train the dialogue model.
Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Data
Further, a model-level distillation process is employed to distill a teacher model trained on high-quality paired data to augmented dialogue pairs, thereby preventing dialogue models from being affected by the noise in the augmented data.
A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data
Further, to incorporate the target persona in the decoding process and to balance its contribution, an attention routing structure is devised in the decoder to merge features extracted from the target persona and dialogue contexts using dynamically predicted weights.
