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Found 25 papers, 17 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).
Improving the Robustness of Knowledge-Grounded Dialogue via Contrastive Learning
Knowledge-grounded dialogue (KGD) learns to generate an informative response based on a given dialogue context and external knowledge (\emph{e. g.}, knowledge graphs; KGs).
M2ConceptBase: A Fine-grained Aligned Multi-modal Conceptual Knowledge Base
To collect concept-image and concept-description alignments, we propose a context-aware multi-modal symbol grounding approach that considers context information in existing large-scale image-text pairs with respect to each concept.
Cross-Lingual Knowledge Editing in Large Language Models
With the recent advancements in large language models (LLMs), knowledge editing has been shown as a promising technique to adapt LLMs to new knowledge without retraining from scratch.
AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities
Multi-modal knowledge graphs (MMKGs) combine different modal data (e. g., text and image) for a comprehensive understanding of entities.
Snowman: A Million-scale Chinese Commonsense Knowledge Graph Distilled from Foundation Model
Constructing commonsense knowledge graphs (CKGs) has attracted wide research attention due to its significant importance in cognitive intelligence.
D$^2$TV: Dual Knowledge Distillation and Target-oriented Vision Modeling for Many-to-Many Multimodal Summarization
Further, we propose a dual knowledge distillation and target-oriented vision modeling framework for the M$^3$S task.
Towards Unifying Multi-Lingual and Cross-Lingual Summarization
In this paper, we aim to unify MLS and CLS into a more general setting, i. e., many-to-many summarization (M2MS), where a single model could process documents in any language and generate their summaries also in any language.
Unified Model Learning for Various Neural Machine Translation
Specifically, we propose a ``versatile'' model, i. e., the Unified Model Learning for NMT (UMLNMT) that works with data from different tasks, and can translate well in multiple settings simultaneously, and theoretically it can be as many as possible.
When to Pre-Train Graph Neural Networks? From Data Generation Perspective!
All convex combinations of graphon bases give rise to a generator space, from which graphs generated form the solution space for those downstream data that can benefit from pre-training.
Is ChatGPT a Good NLG Evaluator? A Preliminary Study
In detail, we regard ChatGPT as a human evaluator and give task-specific (e. g., summarization) and aspect-specific (e. g., relevance) instruction to prompt ChatGPT to evaluate the generated results of NLG models.
Zero-Shot Cross-Lingual Summarization via Large Language Models
Given a document in a source language, cross-lingual summarization (CLS) aims to generate a summary in a different target language.
Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization
However, less attention has been paid to the visual features from the perspective of the summary, which may limit the model performance, especially in the low- and zero-resource scenarios.
Understanding Translationese in Cross-Lingual Summarization
In detail, we find that (1) the translationese in documents or summaries of test sets might lead to the discrepancy between human judgment and automatic evaluation; (2) the translationese in training sets would harm model performance in real-world applications; (3) though machine-translated documents involve translationese, they are very useful for building CLS systems on low-resource languages under specific training strategies.
Long-Document Cross-Lingual Summarization
Cross-Lingual Summarization (CLS) aims at generating summaries in one language for the given documents in another language.
Towards Unifying Reference Expression Generation and Comprehension
Additionally, IRTF could generate pseudo input regions for the REC task to enable a uniform way for sharing the identical representation space across the REC and REG.
GOAL: Towards Benchmarking Few-Shot Sports Game Summarization
Sports game summarization aims to generate sports news based on real-time commentaries.
RT-KGD: Relation Transition Aware Knowledge-Grounded Dialogue Generation
Nevertheless, the correlations between knowledge implied in the multi-turn context and the transition regularities between relations in KGs are under-explored.
A Survey on Cross-Lingual Summarization
Cross-lingual summarization is the task of generating a summary in one language (e. g., English) for the given document(s) in a different language (e. g., Chinese).
ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization
We present ClidSum, a benchmark dataset for building cross-lingual summarization systems on dialogue documents.
Incorporating Commonsense Knowledge into Story Ending Generation via Heterogeneous Graph Networks
Story ending generation is an interesting and challenging task, which aims to generate a coherent and reasonable ending given a story context.
Knowledge Enhanced Sports Game Summarization
Additionally, we also introduce a knowledge-enhanced summarizer that utilizes both live commentaries and the knowledge to generate sports news.
SportsSum2.0: Generating High-Quality Sports News from Live Text Commentary
Sports game summarization aims to generate news articles from live text commentaries.
Enhancing Visual Dialog Questioner with Entity-based Strategy Learning and Augmented Guesser
To enhance VD Questioner: 1) we propose a Related entity enhanced Questioner (ReeQ) that generates questions under the guidance of related entities and learns entity-based questioning strategy from human dialogs; 2) we propose an Augmented Guesser (AugG) that is strong and is optimized for the VD setting especially.
Multi-Modal Chorus Recognition for Improving Song Search
In addition, our approach also helps to improve the accuracy of its downstream task - song search by more than 10. 6%.
