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Alfio Gliozzo

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4 papers

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AAAI Conference 2021 System Paper

KAAPA: Knowledge Aware Answers from PDF Analysis

Nicolas Fauceglia
Mustafa Canim
Alfio Gliozzo
Jennifer J Liang
Nancy Xin Ru Wang
Douglas Burdick
Nandana Mihindukulasooriya
Vittorio Castelli

We present KaaPa (Knowledge Aware Answers from Pdf Analysis), an integrated solution for machine reading comprehension over both text and tables extracted from PDFs. KaaPa enables interactive question refinement using facets generated from an automatically induced Knowledge Graph. In addition it provides a concise summary of the supporting evidence for the provided answers by aggregating information across multiple sources. KaaPa can be applied consistently to any collection of documents in English with zero domain adaptation effort. We showcase the use of KaaPa for QA on scientific literature using the COVID-19 Open Research Dataset.

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AAAI Conference 2020 Conference Paper

Hypernym Detection Using Strict Partial Order Networks

Sarthak Dash
Md Faisal Mahbub Chowdhury
Alfio Gliozzo
Nandana Mihindukulasooriya
Nicolas Rodolfo Fauceglia

This paper introduces Strict Partial Order Networks (SPON), a novel neural network architecture designed to enforce asymmetry and transitive properties as soft constraints. We apply it to induce hypernymy relations by training with is-a pairs. We also present an augmented variant of SPON that can generalize type information learned for in-vocabulary terms to previously unseen ones. An extensive evaluation over eleven benchmarks across different tasks shows that SPON consistently either outperforms or attains the state of the art on all but one of these benchmarks.

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AAAI Conference 2020 Conference Paper

Translucent Answer Predictions in Multi-Hop Reading Comprehension

G P Shrivatsa Bhargav
Michael Glass
Dinesh Garg
Shirish Shevade
Saswati Dana
Dinesh Khandelwal
L Venkata Subramaniam
Alfio Gliozzo

Research on the task of Reading Comprehension style Question Answering (RCQA) has gained momentum in recent years due to the emergence of human annotated datasets and associated leaderboards, for example CoQA, HotpotQA, SQuAD, TriviaQA, etc. While state-of-the-art has advanced considerably, there is still ample opportunity to advance it further on some important variants of the RCQA task. In this paper, we propose a novel deep neural architecture, called TAP (Translucent Answer Prediction), to identify answers and evidence (in the form of supporting facts) in an RCQA task requiring multi-hop reasoning. TAP comprises two loosely coupled networks – Local and Global Interaction eXtractor (LoGIX) and Answer Predictor (AP). LoGIX predicts supporting facts, whereas AP consumes these predicted supporting facts to predict the answer span. The novel design of LoGIX is inspired by two key design desiderata – local context and global interaction– that we identiﬁed by analyzing examples of multi-hop RCQA task. The loose coupling between LoGIX and the AP reveals the set of sentences used by the AP in predicting an answer. Therefore, answer predictions of TAP can be interpreted in a translucent manner. TAP offers state-of-the-art performance on the HotpotQA (Yang et al. 2018) dataset – an apt dataset for multi-hop RCQA task – as it occupies Rank-1 on its leaderboard (https: //hotpotqa. github. io/) at the time of submission.

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AAAI Conference 2019 Short Paper

Learning to Transfer Relational Representations through Analogy

Gaetano Rossiello
Alfio Gliozzo
Michael Glass

We propose a novel approach to learn representations of relations expressed by their textual mentions. In our assumption, if two pairs of entities belong to the same relation, then those two pairs are analogous. We collect a large set of analogous pairs by matching triples in knowledge bases with web-scale corpora through distant supervision. This dataset is adopted to train a hierarchical siamese network in order to learn entity-entity embeddings which encode relational information through the different linguistic paraphrasing expressing the same relation. The model can be used to generate pre-trained embeddings which provide a valuable signal when integrated into an existing neural-based model by outperforming the state-of-the-art methods on a relation extraction task.

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