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Found 27 papers, 17 papers with code
CM-Gen: A Neural Framework for Chinese Metaphor Generation with Explicit Context Modelling
The metaphor identification module is able to perform a self-training procedure, which discovers novel metaphors from a large-scale unlabeled corpus for NM generation.
Evaluating Large Language Models for Generalization and Robustness via Data Compression
We measure: 1) the compression performance on the testing period as a measure of generalization on unseen data; and 2) the performance gap between the training and testing period as a measure of robustness.
Finding Challenging Metaphors that Confuse Pretrained Language Models
In this paper, we test various NLP models on the VUA metaphor dataset and quantify to what extent metaphors affect models' performance on various downstream tasks.
LatestEval: Addressing Data Contamination in Language Model Evaluation through Dynamic and Time-Sensitive Test Construction
LatestEval avoids data contamination by only using texts published within a recent time window, ensuring no overlap with the training corpora of pre-trained language models.
ACL Anthology Helper: A Tool to Retrieve and Manage Literature from ACL Anthology
This paper presents a tool called ``ACL Anthology Helper''.
An Open Source Data Contamination Report for Large Language Models
We also introduce an open-source pipeline that enables the community to perform contamination analysis on customised data and models.
Compressing Context to Enhance Inference Efficiency of Large Language Models
This paper proposes a method called Selective Context that enhances the inference efficiency of LLMs by identifying and pruning redundancy in the input context to make the input more compact.
GPTEval: A Survey on Assessments of ChatGPT and GPT-4
The emergence of ChatGPT has generated much speculation in the press about its potential to disrupt social and economic systems.
MOFO: MOtion FOcused Self-Supervision for Video Understanding
Despite the importance of motion in supervised learning techniques for action recognition, SSL methods often do not explicitly consider motion information in videos.
Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation
Further analysis also shows that our representation learning framework can fill the semantic gap by coagulating representations of both text and graph knowledge.
Metaphor Detection with Effective Context Denoising
We propose a novel RoBERTa-based model, RoPPT, which introduces a target-oriented parse tree structure in metaphor detection.
FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning
In this paper, we propose FrameBERT, a RoBERTa-based model that can explicitly learn and incorporate FrameNet Embeddings for concept-level metaphor detection.
The Secret of Metaphor on Expressing Stronger Emotion
Metaphors are proven to have stronger emotional impact than literal expressions.
Terminology-aware Medical Dialogue Generation
In this paper, we propose a novel framework to improve medical dialogue generation by considering features centered on domain-specific terminology.
EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention
One of the key challenges of automatic story generation is how to generate a long narrative that can maintain fluency, relevance, and coherence.
NGEP: A Graph-based Event Planning Framework for Story Generation
To improve the performance of long text generation, recent studies have leveraged automatically planned event structures (i. e. storylines) to guide story generation.
Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics
In spite of the success of prior works with the application of pre-trained models, current neural models for Chinese stories still struggle to generate high-quality long text narratives.
Recent Advances in Neural Text Generation: A Task-Agnostic Survey
In recent years, considerable research has been dedicated to the application of neural models in the field of natural language generation (NLG).
Human-like Relational Models for Activity Recognition in Video
We apply the method to a challenging subset of the something-something dataset and achieve a more robust performance against neural network baselines on challenging activities.
Interpreting Verbal Metaphors by Paraphrasing
Metaphorical expressions are difficult linguistic phenomena, challenging diverse Natural Language Processing tasks.
Combining Pre-trained Word Embeddings and Linguistic Features for Sequential Metaphor Identification
The pre-trained word embeddings GloVe, ELMo and BERT have individually shown good performance on sequential metaphor identification.
Projection: A Mechanism for Human-like Reasoning in Artificial Intelligence
Artificial Intelligence systems cannot yet match human abilities to apply knowledge to situations that vary from what they have been programmed for, or trained for.
BERT-hLSTMs: BERT and Hierarchical LSTMs for Visual Storytelling
Visual storytelling is a creative and challenging task, aiming to automatically generate a story-like description for a sequence of images.
Ranked #21 on
Visual Storytelling
on VIST
(CIDEr metric)
Latent Space Factorisation and Manipulation via Matrix Subspace Projection
We demonstrate the utility of our method for attribute manipulation in autoencoders trained across varied domains, using both human evaluation and automated methods.
Ranked #7 on
Image Generation
on CelebA 256x256
(FID metric)
End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories
End-to-end training with Deep Neural Networks (DNN) is a currently popular method for metaphor identification.
Word Embedding and WordNet Based Metaphor Identification and Interpretation
Metaphoric expressions are widespread in natural language, posing a significant challenge for various natural language processing tasks such as Machine Translation.
Analysing the Causes of Depressed Mood from Depression Vulnerable Individuals
We develop a computational model to discover the potential causes of depression by analysing the topics in a usergenerated text.
