Pablo Loyola


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Perceptual Structure in the absence of grounding: the impact of abstractedness and subjectivity in color language for LLMs
Pablo Loyola | Edison Marrese-Taylor | Andres Hoyos-Idrobo
Findings of the Association for Computational Linguistics: EMNLP 2023

The need for grounding in language understanding is an active research topic. Previous work has suggested that color perception and color language appear as a suitable test bed to empirically study the problem, given its cognitive significance and showing that there is considerable alignment between a defined color space and the feature space defined by a language model. To further study this issue, we collect a large scale source of colors and their descriptions, containing almost a 1 million examples , and perform an empirical analysis to compare two kinds of alignments: (i) inter-space, by learning a mapping between embedding space and color space, and (ii) intra-space, by means of prompting comparatives between color descriptions. Our results show that while color space alignment holds for monolexemic, highly pragmatic color descriptions, this alignment drops considerably in the presence of examples that exhibit elements of real linguistic usage such as subjectivity and abstractedness, suggesting that grounding may be required in such cases.


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Learning to Describe Editing Activities in Collaborative Environments: A Case Study on GitHub and Wikipedia
Edison Marrese-Taylor | Pablo Loyola | Jorge A. Balazs | Yutaka Matsuo
Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation


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An Edit-centric Approach for Wikipedia Article Quality Assessment
Edison Marrese-Taylor | Pablo Loyola | Yutaka Matsuo
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)

We propose an edit-centric approach to assess Wikipedia article quality as a complementary alternative to current full document-based techniques. Our model consists of a main classifier equipped with an auxiliary generative module which, for a given edit, jointly provides an estimation of its quality and generates a description in natural language. We performed an empirical study to assess the feasibility of the proposed model and its cost-effectiveness in terms of data and quality requirements.


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Villani at SemEval-2018 Task 8: Semantic Extraction from Cybersecurity Reports using Representation Learning
Pablo Loyola | Kugamoorthy Gajananan | Yuji Watanabe | Fumiko Satoh
Proceedings of the 12th International Workshop on Semantic Evaluation

In this paper, we describe our proposal for the task of Semantic Extraction from Cybersecurity Reports. The goal is to explore if natural language processing methods can provide relevant and actionable knowledge to contribute to better understand malicious behavior. Our method consists of an attention-based Bi-LSTM which achieved competitive performance of 0.57 for the Subtask 1. In the due process we also present ablation studies across multiple embeddings and their level of representation and also report the strategies we used to mitigate the extreme imbalance between classes.

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Content Aware Source Code Change Description Generation
Pablo Loyola | Edison Marrese-Taylor | Jorge Balazs | Yutaka Matsuo | Fumiko Satoh
Proceedings of the 11th International Conference on Natural Language Generation

We propose to study the generation of descriptions from source code changes by integrating the messages included on code commits and the intra-code documentation inside the source in the form of docstrings. Our hypothesis is that although both types of descriptions are not directly aligned in semantic terms —one explaining a change and the other the actual functionality of the code being modified— there could be certain common ground that is useful for the generation. To this end, we propose an architecture that uses the source code-docstring relationship to guide the description generation. We discuss the results of the approach comparing against a baseline based on a sequence-to-sequence model, using standard automatic natural language generation metrics as well as with a human study, thus offering a comprehensive view of the feasibility of the approach.


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A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes
Pablo Loyola | Edison Marrese-Taylor | Yutaka Matsuo
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We propose a model to automatically describe changes introduced in the source code of a program using natural language. Our method receives as input a set of code commits, which contains both the modifications and message introduced by an user. These two modalities are used to train an encoder-decoder architecture. We evaluated our approach on twelve real world open source projects from four different programming languages. Quantitative and qualitative results showed that the proposed approach can generate feasible and semantically sound descriptions not only in standard in-project settings, but also in a cross-project setting.

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Refining Raw Sentence Representations for Textual Entailment Recognition via Attention
Jorge Balazs | Edison Marrese-Taylor | Pablo Loyola | Yutaka Matsuo
Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP

In this paper we present the model used by the team Rivercorners for the 2017 RepEval shared task. First, our model separately encodes a pair of sentences into variable-length representations by using a bidirectional LSTM. Later, it creates fixed-length raw representations by means of simple aggregation functions, which are then refined using an attention mechanism. Finally it combines the refined representations of both sentences into a single vector to be used for classification. With this model we obtained test accuracies of 72.057% and 72.055% in the matched and mismatched evaluation tracks respectively, outperforming the LSTM baseline, and obtaining performances similar to a model that relies on shared information between sentences (ESIM). When using an ensemble both accuracies increased to 72.247% and 72.827% respectively.