Papers by André Freitas
Building a Knowledge Graph from Natural Language Definitions for Interpretable Text Entailment Recognition (L18-1)
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| Challenge: | a conceptual model for dictionary definitions is used to construct a knowledge graph from natural language definitions. |
| Approach: | They propose a method for automatically building a graph world knowledge base from natural language definitions. |
| Outcome: | The proposed method was used in an interpretable text entailment recognition approach. |
Estimating the Causal Effects of Natural Logic Features in Transformer-Based NLI Models (2024.lrec-main)
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| Challenge: | a number of studies have reported high accuracies in NLP tasks due to simple heuristics and dataset artifacts. |
| Approach: | They use a case where two words/terms occur in a shared context to construct a causal diagram . they also investigate the robustness to irrelevant changes and sensitivity to impactful changes of Transformers . |
| Outcome: | The proposed method bolsters the fact that similar benchmark accuracy scores may be observed for models that exhibit very different behaviour. |
DBee: A Database for Creating and Managing Knowledge Graphs and Embeddings (D19-53)
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| Challenge: | DBee provides a data model which operates over knowledge graphs and embedding vector spaces . |
| Approach: | They describe a database which provides a data model which exploits the semantic properties of large-scale knowledge graphs and embedding vector spaces. |
| Outcome: | The proposed model exploits the semantic properties of both types of representations. |
Graphene: a Context-Preserving Open Information Extraction System (C18-2)
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| Challenge: | Graphene is an open IE system that generates accurate, meaningful and complete propositions . current systems tend to extract propositions with long argument phrases that can be further decomposed into meaningful propositions, with each of them representing a separate fact. |
| Approach: | They propose a lightweight Open IE system that generates accurate, meaningful propositions . they identify the rhetorical relations that hold between them to maintain their semantic relationship . |
| Outcome: | The proposed system generates propositions that are accurate, meaningful and complete . it preserves the context of the relational tuples extracted from the source sentence . |
Montague semantics and modifier consistency measurement in neural language models (2025.coling-main)
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| Challenge: | Existing studies on distributional language models have been focused on linguistics and their relationship with semantic formalisms for decades. |
| Approach: | They propose a method for measuring compositional behavior in contemporary language embedding models by introducing three new tests inspired by Montague semantics. |
| Outcome: | The proposed method measures compositional behavior in language embedding models on adjectival modifier phenomena in adjective-noun phrases. |
Unification-based Reconstruction of Multi-hop Explanations for Science Questions (2021.eacl-main)
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| Challenge: | Existing approaches build explanations considering each question in isolation, but new approach leverages explanatory patterns emerging in scientific explanations. |
| Approach: | They propose a framework for reconstructing multi-hop explanations in science Question Answering . they integrate lexical relevance with the notion of unification power to rank atomic facts . |
| Outcome: | The proposed method achieves results competitive with Transformers, but is faster and scalable to large explanatory corpora. |
Transforming Complex Sentences into a Semantic Hierarchy (P19-1)
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| Challenge: | Existing approaches for recursively splitting and rephrasing complex English sentences into a semantic hierarchy of simplified sentences are lacking. |
| Approach: | They propose a method for recursively splitting and rephrasing complex English sentences into a semantic hierarchy of simplified sentences. |
| Outcome: | The proposed approach outperforms state-of-the-art approaches in MT and information extraction tasks. |
Identifying and Explaining Discriminative Attributes (D19-1)
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| Challenge: | Existing word vector representation models lack latent features (dense vectors) identifying discriminative attributes can motivate the development of word vector models with finer semantics. |
| Approach: | They propose to use a word vector representation model to identify discriminative attributes by combining knowledge graphs with images to construct explicit vector spaces. |
| Outcome: | The proposed model performs comparable to state-of-the-art systems while providing full model transparency and explainability. |
Formal Semantic Controls over Language Models (2024.lrec-tutorials)
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| Challenge: | Text embeddings provide a concise representation of the semantics of sentences and larger spans of text, rather than individual words, capturing a wide range of linguistic features. |
| Approach: | They propose to shorten the gap between latent semantics and formal symbolics by comparing distributional models to symbolic models grounded on formal linguistics and well-defined mathematical properties. |
| Outcome: | This paper examines the analysis and control of text representations, covering methods from pooling to LLM-based. |
PhysNLU: A Language Resource for Evaluating Natural Language Understanding and Explanation Coherence in Physics (2022.lrec-1)
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| Challenge: | physicists use mathematics to reason and explain, separates their field from other disciplines, including mathematics. |
| Approach: | They present a dataset to evaluate the performance of language models in physics . they find that language models are challenged by coherence related tasks in physicists . |
| Outcome: | The proposed models are able to perform well on coherence-related tasks even when trained on natural language objectives. |
Does My Representation Capture X? Probe-Ably (2021.acl-demo)
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| Challenge: | Probing (or diagnostic classification) has become a popular strategy for investigating whether a given set of intermediate features is present in the representations of neural models. |
| Approach: | They propose to use an extendable probing framework to automate the application of probing methods to the user’s inputs. |
| Outcome: | The proposed framework automates the application of probing methods to the user’s inputs. |
A Multilingual Test Collection for the Semantic Search of Entity Categories (L18-1)
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Juliano Efson Sales, Siamak Barzegar, Wellington Franco, Bernhard Bermeitinger, Tiago Cunha, Brian Davis, André Freitas, Siegfried Handschuh
| Challenge: | Despite the high popularity of entity search, entity categories have not received equal attention. |
| Approach: | They propose to make public a multilingual test collection comprehending English, Portuguese and German to meet the demands of the entity search community. |
| Outcome: | The proposed test collection comprehends English, Portuguese and German and provides comparative baselines and an analysis of the results. |
Introductory Tutorial: Reasoning with Natural Language Explanations (2024.emnlp-tutorials)
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| Challenge: | Existing paradigms for explanation-based NLIs lack a clear understanding of the nature of human reasoning. |
| Approach: | They propose to use natural language explanations to build models that address downstream tasks through explicit construction of a natural language. |
| Outcome: | In contrast to the existing paradigm based on Deep Learning, explanation-based NLI focuses on developing and evaluating models that address downstream tasks through the explicit construction of a natural language explanation. |
Text Classification and Prediction in the Legal Domain (2022.lrec-1)
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| Challenge: | a case study combines text classification and legal judgment prediction for flight compensation . a human-in-the-loop model outperformed human prediction when predicting a claim being successful . |
| Approach: | They combine transformer-based classification models with human-in-the-loop data to classify airlines' responses to flight compensation claims. |
| Outcome: | The proposed models outperform human prediction when predicting a legal claim success. |
Premise Selection in Natural Language Mathematical Texts (2020.acl-main)
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| Challenge: | Existing tasks for natural language premise selection are limited and difficult for humans to interpret and write. |
| Approach: | They propose to use natural language premise selection task to predict premises that will be useful to prove a particular statement. |
| Outcome: | The proposed approach improves the performance of baselines and multi-hop premise selection tasks. |
Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference (2022.tacl-1)
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| Challenge: | Existing explainable multi-hop inference models are regarded as black-boxes due to their ability to transfer linguistic and semantic information to downstream tasks, posing concerns about interpretability and transparency of their predictions. |
| Approach: | They propose a hybrid framework that integrates explicit constraints with neural architectures through differentiable convex optimization to answer and explain multi-hop questions in natural language. |
| Outcome: | The proposed framework improves performance on scientific and commonsense QA tasks while still providing structured explanations in support of its predictions. |
A Differentiable Integer Linear Programming Solver for Explanation-Based Natural Language Inference (2024.lrec-main)
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| Challenge: | Existing ILP frameworks are non-differentiable and cannot be integrated as part of a broader deep learning architecture. |
| Approach: | They propose a neuro-symbolic architecture for explanation-based NLI based on DBCS. |
| Outcome: | The proposed approach achieves superior performance when compared to existing solvers and black-box solver. |
Introduction to Mathematical Language Processing: Informal Proofs, Word Problems, and Supporting Tasks (2023.tacl-1)
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| Challenge: | Using mathematical language processing methods, we analyze prevailing methods, existing limitations, and promising avenues for future research. |
| Approach: | They analyze mathematical language processing methods from recent years and highlight prevailing methodologies, existing limitations and promising avenues for future research. |
| Outcome: | The proposed methods highlight prevailing methods, existing limitations and promising avenues for future research. |
Learning Disentangled Representations for Natural Language Definitions (2023.findings-eacl)
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| Challenge: | Disentangling the encodings of neural models is a fundamental aspect for improving interpretability, semantic control and downstream task performance in Natural Language Processing. |
| Approach: | They propose to use syntactic and semantic regularities in textual data to provide models with both structural biases and generative factors. |
| Outcome: | The proposed model outperforms baselines on several qualitative and quantitative benchmarks and improves the results in the downstream task of definition modeling. |
Indra: A Word Embedding and Semantic Relatedness Server (L18-1)
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Juliano Efson Sales, Leonardo Souza, Siamak Barzegar, Brian Davis, André Freitas, Siegfried Handschuh
| Challenge: | Word embedding/distributional semantic models are a fundamental component in many natural language processing (NLP) architectures. |
| Approach: | They propose a multi-lingual word embedding/distributional semantics framework which supports creation, use and evaluation of word embedded models. |
| Outcome: | The proposed tool supports the creation, use and evaluation of word embedding models. |
Disentangling Generative Factors in Natural Language with Discrete Variational Autoencoders (2021.findings-emnlp)
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| Challenge: | Disentangled representation learning aims to provide an interpretable representation of latent features and a framework for controlling the change of specific features. |
| Approach: | They propose a Variational Autoencoder based method which models language features as discrete variables and encourages independence between variables for learning disentangled representations. |
| Outcome: | The proposed model outperforms baselines on several qualitative and quantitative benchmarks and on a text style transfer downstream application. |
A Survey on Open Information Extraction (C18-1)
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| Challenge: | Existing approaches to open information extraction (Open IE) focus on narrow, well-defined requests over a predefined set of target relations on small, homogeneous corpora. |
| Approach: | They propose to use unsupervised methods to extract all types of relations found in text . they propose to implement a system that can be automated to detect possible relations . |
| Outcome: | The proposed approaches have been compared with existing methods and are based on the results of a literature review. |
Explainable Inference Over Grounding-Abstract Chains for Science Questions (2021.findings-acl)
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| Challenge: | Existing inference models for science questions are black-box by nature, lacking explanations for their predictions. |
| Approach: | They propose an explainable inference approach for science questions by reasoning on grounding and abstract inference chains. |
| Outcome: | The proposed model generates plausible explanations for science questions using a weighted graph of relevant facts and a Bayesian Optimisation formalism. |
Natural Language Premise Selection: Finding Supporting Statements for Mathematical Text (2020.lrec-1)
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| Challenge: | Existing approaches to understand mathematical discourse are limited by the complexity of word and symbol interactions. |
| Approach: | They propose a task to retrieve supporting definitions and supporting propositions from a dataset that can be used to evaluate different approaches for the task. |
| Outcome: | The proposed task is based on a dataset that can be used to evaluate different approaches for the natural premise selection task. |
Neuro-Symbolic Natural Language Processing (2025.emnlp-tutorials)
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| Challenge: | Large Language Models (LLMs) have limitations in terms of safe and controlled reasoning, interpretability and adaptability . this tutorial aims to bridge the gap between the practical performance of LLMs and the principled modelling of language and inference of formal methods. |
| Approach: | This tutorial aims to bridge the gap between the practical performance of Large Language Models and the principled modelling of language and inference of formal methods. |
| Outcome: | This tutorial aims to bridge the gap between the performance of LLMs and the principled modelling of language and inference of formal methods. |
The SSIX Corpora: Three Gold Standard Corpora for Sentiment Analysis in English, Spanish and German Financial Microblogs (L18-1)
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| Challenge: | SSIX corpora provide annotated data for supervised learning methods . polarity annotation is performed on two financial microblog platforms . |
| Approach: | They propose three SSIX corpora for sentiment analysis which provide annotated data for supervised learning methods. |
| Outcome: | The proposed corpora are in English, German and Spanish. |
Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks (D19-53)
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| Challenge: | Recent advances in reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text. |
| Approach: | They propose a document-structured message passing architecture for the identification of supporting facts over a graph-structure based representation of text. |
| Outcome: | The proposed model outperforms a baseline reading comprehension test on raw text and shows that it is relevant for multi-hop reasoning. |
STAR: Cross-modal [STA]tement [R]epresentation for selecting relevant mathematical premises (2021.eacl-main)
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| Challenge: | Existing representations of mathematical statements in natural language are ineffective . STAR model uses cross-modal attention to represent mathematical text . |
| Approach: | They propose a model that uses cross-modal attention to represent mathematical text . it uses conjectures written in both natural and mathematical language to recommend premises . |
| Outcome: | The proposed model outperforms baseline models that do not distinguish between natural and mathematical elements and achieves better performance than state-of-the-art models. |
Interventional Probing in High Dimensions: An NLI Case Study (2023.findings-eacl)
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| Challenge: | Probing strategies have been shown to detect the presence of various linguistic features inlarge language models; in particular, semantic features intermediate to the “natural logic”fragment of the NLI. |
| Approach: | They propose to use amnesic probing and mnestic probing to investigate the effect of these semantic fea-tures on NLI classification by examining the effects of a mnemonic probing variation on the model. |
| Outcome: | The proposed methods have been shown to detect features intermediate to the “natural logic”fragment of the Natural Language Inferencetask (NLI). |
Case-Based Abductive Natural Language Inference (2022.coling-1)
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| Challenge: | Recent approaches for multi-hop inference construct explanations considering each test case in isolation, but semantic drift causes wrong conclusions. |
| Approach: | They propose an abductive framework for multi-hop NLI exploring the retrieve-reuse-refine paradigm in Case-Based Reasoning. |
| Outcome: | The proposed model can be integrated with sparse and dense pre-trained encoders to improve multi-hop inference, or adopted as an evidence retriever for Transformers. |
A Survey of Text Games for Reinforcement Learning Informed by Natural Language (2022.tacl-1)
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| Challenge: | Interactive Fiction Games (text games) are a problem type that require natural language to solve complex tasks. |
| Approach: | They propose to use interactive fiction games as a testing environment to test the new Reinforcement Learning solutions using natural language. |
| Outcome: | The proposed solutions are based on the proposed interactive fiction games and the generated environments. |
Graphene: Semantically-Linked Propositions in Open Information Extraction (C18-1)
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| Challenge: | Existing Open IE systems focus on identifying and extracting relations of interest, but this manual labor scales linearly with the number of target relations. |
| Approach: | They propose an Open Information Extraction approach that uses a two-layered transformation stage and rhetorical relation identification to transform sentences into syntactically sound sentences. |
| Outcome: | The proposed approach outperforms state-of-the-art Open IE systems in the construction of correct n-ary predicate-argument structures. |