345 research outputs found
"Con sanctissima pompa". Lo spettacolo sacro a Ferrara nel XV secolo (1429-1505)
The thesis investigates the birth and development of religious theater in Ferrara in the Fifteenth
Century. Starting from the Chronicles of the period and the payment records of Camera Ducale
Estense, held at the State Archives in Modena, it reconstructs the material culture of theater. The
performances are analyzed in the dialectics between the various cultures in Ferrara: religious,
literary and courteous. It shows a variety of styles, shapes and structures that reveals the theater as
the place of cultural contamination
Rethink the Effectiveness of Text Data Augmentation: An Empirical Analysis
In recent years, language models (LMs) have made remarkable progress in
advancing the field of natural language processing (NLP). However, the impact
of data augmentation (DA) techniques on the fine-tuning (FT) performance of
these LMs has been a topic of ongoing debate. In this study, we evaluate the
effectiveness of three different FT methods in conjugation with
back-translation across an array of 7 diverse NLP tasks, including
classification and regression types, covering single-sentence and sentence-pair
tasks. Contrary to prior assumptions that DA does not contribute to the
enhancement of LMs' FT performance, our findings reveal that continued
pre-training on augmented data can effectively improve the FT performance of
the downstream tasks. In the most favourable case, continued pre-training
improves the performance of FT by more than 10% in the few-shot learning
setting. Our finding highlights the potential of DA as a powerful tool for
bolstering LMs' performance
Priming and Actions: An Analysis in Conversational Search Systems
In order to accurately simulate users in conversational systems, it is essential to comprehend the factors that influence their behaviour. This is a critical challenge for the Information Retrieval (IR) field, as conventional methods are not well-suited for the interactive and unique sequential structure of conversational contexts. In this study, we employed the concept of Priming effects from the Psychology literature to identify core stimuli for each abstracted effect. We then examined these stimuli on various datasets to investigate their correlations with users' actions. Finally, we trained Logistic Regression (LR) models based on these stimuli to anticipate users' actions. Our findings offer a basis for creating more realistic user models and simulators, as we identified the subset of stimuli with strong relationships with users' actions. Additionally, we built a model that can predict users' actions
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning
Prompt tuning (PT), where a small amount of trainable soft (continuous)
prompt vectors is affixed to the input of language models (LM), has shown
promising results across various tasks and models for parameter-efficient
fine-tuning (PEFT). PT stands out from other PEFT approaches because it
maintains competitive performance with fewer trainable parameters and does not
drastically scale up its parameters as the model size expands. However, PT
introduces additional soft prompt tokens, leading to longer input sequences,
which significantly impacts training and inference time and memory usage due to
the Transformer's quadratic complexity. Particularly concerning for Large
Language Models (LLMs) that face heavy daily querying. To address this issue,
we propose Decomposed Prompt Tuning (DePT), which decomposes the soft prompt
into a shorter soft prompt and a pair of low-rank matrices that are then
optimised with two different learning rates. This allows DePT to achieve better
performance while saving over 20% memory and time costs compared to vanilla PT
and its variants, without changing trainable parameter sizes. Through extensive
experiments on 23 natural language processing (NLP) and vision-language (VL)
tasks, we demonstrate that DePT outperforms state-of-the-art PEFT approaches,
including the full fine-tuning baseline in some scenarios. Additionally, we
empirically show that DEPT grows more efficient as the model size increases.
Our further study reveals that DePT integrates seamlessly with
parameter-efficient transfer learning in the few-shot learning setting and
highlights its adaptability to various model architectures and sizes.Comment: Code is available at https://github.com/ZhengxiangShi/DeP
Rethink the Effectiveness of Text Data Augmentation: An Empirical Analysis
In recent years, language models (LMs) have made remarkable progress in
advancing the field of natural language processing (NLP). However, the impact
of data augmentation (DA) techniques on the fine-tuning (FT) performance of
these LMs has been a topic of ongoing debate. In this study, we evaluate the
effectiveness of three different FT methods in conjugation with
back-translation across an array of 7 diverse NLP tasks, including
classification and regression types, covering single-sentence and sentence-pair
tasks. Contrary to prior assumptions that DA does not contribute to the
enhancement of LMs' FT performance, our findings reveal that continued
pre-training on augmented data can effectively improve the FT performance of
the downstream tasks. In the most favourable case, continued pre-training
improves the performance of FT by more than 10% in the few-shot learning
setting. Our finding highlights the potential of DA as a powerful tool for
bolstering LMs' performance.Comment: Accepted at ESANN 202
Convolutional Neural Networks for Water segmentation using Sentinel-2 Red, Green, Blue (RGB) composites and derived Spectral Indices
Near-real time water segmentation with medium resolution satellite imagery plays a critical role in water management. Automated water segmentation of satellite imagery has traditionally been achieved using spectral indices. Spectral water segmentation is limited by environmental factors and requires human expertise to be applied effectively. In recent years, the use of convolutional neural networks (CNN’s) for water segmentation has been successful when used on high-resolution satellite imagery, but to a lesser extent for medium resolution imagery. Existing studies have been limited to geographically localized datasets and reported metrics have been benchmarked against a limited range of spectral indices. This study seeks to determine if a single CNN based on Red, Green, Blue (RGB) image classification can effectively segment water on a global scale and outperform traditional spectral methods. Additionally, this study evaluates the extent to which smaller datasets (of very complex pattern, e.g harbour megacities) can be used to improve globally applicable CNNs within a specific region. Multispectral imagery from the European Space Agency, Sentinel-2 satellite (10 m spatial resolution) was sourced. Test sites were selected in Florida, New York, and Shanghai to represent a globally diverse range of waterbody typologies. Region-specific spectral water segmentation algorithms were developed on each test site, to represent benchmarks of spectral index performance. DeepLabV3-ResNet101 was trained on 33,311 semantically labelled true-colour samples. The resulting model was retrained on three smaller subsets of the data, specific to New York, Shanghai and Florida. CNN predictions reached a maximum mean intersection over union result of 0.986 and F1-Score of 0.983. At the Shanghai test site, the CNN’s predictions outperformed the spectral benchmark, primarily due to the CNN’s ability to process contextual features at multiple scales. In all test cases, retraining the networks to localized subsets of the dataset improved the localized region’s segmentation predictions. The CNN’s presented are suitable for cloud-based deployment and could contribute to the wider use of satellite imagery for water management
Word-final velar place assimilation in English
In English, word-final alveolar consonants assimilate in place. Additionally, there is recent evidence that assimilation can occur in word-final nasals at all places of articulation (Coleman et al. 2016). Some anecdotal evidence exists that word-final velars can assimilate (Barry 1985), but this has not been substantiated. This study uses the Santa Barbara Corpus of American English (DuBois et al. 2000, 2005) to examine word-final velar consonant variation, which was measured by the F2 transitions in the preceding vowel. Given the present data, word-final velars do not seem to undergo categorical assimilation or gradient coarticulation processes
Self Contrastive Learning for Session-based Recommendation
Session-based recommendation, which aims to predict the next item of users'
interest as per an existing sequence interaction of items, has attracted
growing applications of Contrastive Learning (CL) with improved user and item
representations. However, these contrastive objectives: (1) serve a similar
role as the cross-entropy loss while ignoring the item representation space
optimisation; and (2) commonly require complicated modelling, including complex
positive/negative sample constructions and extra data augmentation. In this
work, we introduce Self-Contrastive Learning (SCL), which simplifies the
application of CL and enhances the performance of state-of-the-art CL-based
recommendation techniques. Specifically, SCL is formulated as an objective
function that directly promotes a uniform distribution among item
representations and efficiently replaces all the existing contrastive objective
components of state-of-the-art models. Unlike previous works, SCL eliminates
the need for any positive/negative sample construction or data augmentation,
leading to enhanced interpretability of the item representation space and
facilitating its extensibility to existing recommender systems. Through
experiments on three benchmark datasets, we demonstrate that SCL consistently
improves the performance of state-of-the-art models with statistical
significance. Notably, our experiments show that SCL improves the performance
of two best-performing models by 8.2% and 9.5% in P@10 (Precision) and 9.9% and
11.2% in MRR@10 (Mean Reciprocal Rank) on average across different benchmarks.
Additionally, our analysis elucidates the improvement in terms of alignment and
uniformity of representations, as well as the effectiveness of SCL with a low
computational cost.Comment: Technical Repor
Learning to execute or ask clarification questions
Collaborative tasks are ubiquitous activities where a form of communication is required in order to reach a joint goal. Collaborative building is one of such tasks. We wish to develop an intelligent builder agent in a simulated building environment (Minecraft) that can build whatever users wish to build by just talking to the agent. In order to achieve this goal, such agents need to be able to take the initiative by asking clarification questions when further information is needed. Existing works on Minecraft Corpus Dataset only learn to execute instructions neglecting the importance of asking for clarifications. In this paper, we extend the Minecraft Corpus Dataset by annotating all builder utterances into eight types, including clarification questions, and propose a new builder agent model capable of determining when to ask or execute instructions. Experimental results show that our model achieves state-of-the-art performance on the collaborative building task with a substantial improvement. We also define two new tasks, the learning to ask task and the joint learning task. The latter consists of solving both collaborating building and learning to ask tasks jointly
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