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Found 12 papers, 8 papers with code
Revisit Systematic Generalization via Meaningful Learning
Humans can systematically generalize to novel compositions of existing concepts.
Recurrent Inference in Text Editing
In neural text editing, prevalent sequence-to-sequence based approaches directly map the unedited text either to the edited text or the editing operations, in which the performance is degraded by the limited source text encoding and long, varying decoding steps.
Incorporating External POS Tagger for Punctuation Restoration
Punctuation restoration is an important post-processing step in automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Counterfactual Adversarial Learning with Representation Interpolation
Deep learning models exhibit a preference for statistical fitting over logical reasoning.
Text Editing as Imitation Game
Text editing, such as grammatical error correction, arises naturally from imperfect textual data.
RoChBert: Towards Robust BERT Fine-tuning for Chinese
Despite of the superb performance on a wide range of tasks, pre-trained language models (e. g., BERT) have been proved vulnerable to adversarial texts.
Interactive Natural Language Processing
Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP, aimed at addressing limitations in existing frameworks while aligning with the ultimate goals of artificial intelligence.
Don't Trust ChatGPT when Your Question is not in English: A Study of Multilingual Abilities and Types of LLMs
In this work, we propose a systematic way of qualifying the performance disparities of LLMs under multilingual settings.
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework
In our experiments, we conduct a robustness evaluation of RoBERTa models to demonstrate the effectiveness of our evaluation framework, and further show the rationality of each component in the framework.
UAlberta at SemEval-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense Disambiguation
We present a novel algorithm that leverages glosses retrieved from BabelNet, in combination with text and image encoders.
Lost in Translation: When GPT-4V(ision) Can't See Eye to Eye with Text. A Vision-Language-Consistency Analysis of VLLMs and Beyond
Expanding on our findings, we introduce "Vision Description Prompting," a method that effectively improves performance in challenging vision-related tasks.
Optimizing Negative Prompts for Enhanced Aesthetics and Fidelity in Text-To-Image Generation
In text-to-image generation, using negative prompts, which describe undesirable image characteristics, can significantly boost image quality.
