17 citations · 27 across the 15 of their papers we have counts for
15 papers
Towards Controllable Natural Language Inference through Lexical Inference Types
Yingji Zhang, Danilo S. Carvalho, Ian Pratt-Hartmann +1
Explainable natural language inference aims to provide a mechanism to produce explanatory (abductive) inference chains which ground claims to their supporting premises. A recent co…
Estimating the Causal Effects of Natural Logic Features in Neural NLI Models
Julia Rozanova, Marco Valentino, Andre Freitas
Rigorous evaluation of the causal effects of semantic features on language model predictions can be hard to achieve for natural language reasoning problems. However, this is such a…
A Symbolic Framework for Evaluating Mathematical Reasoning and Generalisation with Transformers
Jordan Meadows, Marco Valentino, Damien Teney +1
This paper proposes a methodology for generating and perturbing detailed derivations of equations at scale, aided by a symbolic engine, to evaluate the generalisability of Transfor…
A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation
Xiaowei Huang, Wenjie Ruan, Wei Huang +14
Large Language Models (LLMs) have exploded a new heatwave of AI for their ability to engage end-users in human-level conversations with detailed and articulate answers across many…
Interventional Probing in High Dimensions: An NLI Case Study
Julia Rozanova, Marco Valentino, Lucas Cordeiro +1
Probing strategies have been shown to detect the presence of various linguistic features in large language models; in particular, semantic features intermediate to the "natural log…
Montague semantics and modifier consistency measurement in neural language models
Danilo S. Carvalho, Edoardo Manino, Julia Rozanova +2
This work proposes a novel methodology for measuring compositional behavior in contemporary language embedding models. Specifically, we focus on adjectival modifier phenomena in ad…
Going Beyond Approximation: Encoding Constraints for Explainable Multi-hop Inference via Differentiable Combinatorial Solvers
Mokanarangan Thayaparan, Marco Valentino, André Freitas
Integer Linear Programming (ILP) provides a viable mechanism to encode explicit and controllable assumptions about explainable multi-hop inference with natural language. However, a…
Systematicity, Compositionality and Transitivity of Deep NLP Models: a Metamorphic Testing Perspective
Edoardo Manino, Julia Rozanova, Danilo Carvalho +2
Metamorphic testing has recently been used to check the safety of neural NLP models. Its main advantage is that it does not rely on a ground truth to generate test cases. However,…
Decomposing Natural Logic Inferences in Neural NLI
Julia Rozanova, Deborah Ferreira, Marco Valentino +2
In the interest of interpreting neural NLI models and their reasoning strategies, we carry out a systematic probing study which investigates whether these models capture the crucia…
A Survey of Text Games for Reinforcement Learning informed by Natural Language
Philip Osborne, Heido Nõmm, Andre Freitas
Reinforcement Learning has shown success in a number of complex virtual environments. However, many challenges still exist towards solving problems with natural language as a core…
Grounding Natural Language Instructions: Can Large Language Models Capture Spatial Information?
Julia Rozanova, Deborah Ferreira, Krishna Dubba +3
Models designed for intelligent process automation are required to be capable of grounding user interface elements. This task of interface element grounding is centred on linking i…
Hybrid Autoregressive Inference for Scalable Multi-hop Explanation Regeneration
Marco Valentino, Mokanarangan Thayaparan, Deborah Ferreira +1
Regenerating natural language explanations in the scientific domain has been proposed as a benchmark to evaluate complex multi-hop and explainable inference. In this context, large…
Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference
Mokanarangan Thayaparan, Marco Valentino, Deborah Ferreira +2
This paper presents Diff-Explainer, the first hybrid framework for explainable multi-hop inference that integrates explicit constraints with neural architectures through differenti…
On the Evaluation of Intelligent Process Automation
Deborah Ferreira, Julia Rozanova, Krishna Dubba +2
Intelligent Process Automation (IPA) is emerging as a sub-field of AI to support the automation of long-tail processes which requires the coordination of tasks across different sys…
Proceedings of the AAAI-20 Workshop on Intelligent Process Automation (IPA-20)
Dell Zhang, Andre Freitas, Dacheng Tao +1
This is the Proceedings of the AAAI-20 Workshop on Intelligent Process Automation (IPA-20) which took place in New York, NY, USA on February 7th 2020.