Mohammad Javad Hosseini


2022

pdf bib
Cross-lingual Inference with A Chinese Entailment Graph
Tianyi Li | Sabine Weber | Mohammad Javad Hosseini | Liane Guillou | Mark Steedman
Findings of the Association for Computational Linguistics: ACL 2022

Predicate entailment detection is a crucial task for question-answering from text, where previous work has explored unsupervised learning of entailment graphs from typed open relation triples. In this paper, we present the first pipeline for building Chinese entailment graphs, which involves a novel high-recall open relation extraction (ORE) method and the first Chinese fine-grained entity typing dataset under the FIGER type ontology. Through experiments on the Levy-Holt dataset, we verify the strength of our Chinese entailment graph, and reveal the cross-lingual complementarity: on the parallel Levy-Holt dataset, an ensemble of Chinese and English entailment graphs outperforms both monolingual graphs, and raises unsupervised SOTA by 4.7 AUC points.

2021

pdf bib
Multi-Level Gazetteer-Free Geocoding
Sayali Kulkarni | Shailee Jain | Mohammad Javad Hosseini | Jason Baldridge | Eugene Ie | Li Zhang
Proceedings of Second International Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics

We present a multi-level geocoding model (MLG) that learns to associate texts to geographic coordinates. The Earth’s surface is represented using space-filling curves that decompose the sphere into a hierarchical grid. MLG balances classification granularity and accuracy by combining losses across multiple levels and jointly predicting cells at different levels simultaneously. It obtains large gains without any gazetteer metadata, demonstrating that it can effectively learn the connection between text spans and coordinates—and thus makes it a gazetteer-free geocoder. Furthermore, MLG obtains state-of-the-art results for toponym resolution on three English datasets without any dataset-specific tuning.

pdf bib
Multivalent Entailment Graphs for Question Answering
Nick McKenna | Liane Guillou | Mohammad Javad Hosseini | Sander Bijl de Vroe | Mark Johnson | Mark Steedman
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Drawing inferences between open-domain natural language predicates is a necessity for true language understanding. There has been much progress in unsupervised learning of entailment graphs for this purpose. We make three contributions: (1) we reinterpret the Distributional Inclusion Hypothesis to model entailment between predicates of different valencies, like DEFEAT(Biden, Trump) entails WIN(Biden); (2) we actualize this theory by learning unsupervised Multivalent Entailment Graphs of open-domain predicates; and (3) we demonstrate the capabilities of these graphs on a novel question answering task. We show that directional entailment is more helpful for inference than non-directional similarity on questions of fine-grained semantics. We also show that drawing on evidence across valencies answers more questions than by using only the same valency evidence.

pdf bib
Open-Domain Contextual Link Prediction and its Complementarity with Entailment Graphs
Mohammad Javad Hosseini | Shay B. Cohen | Mark Johnson | Mark Steedman
Findings of the Association for Computational Linguistics: EMNLP 2021

An open-domain knowledge graph (KG) has entities as nodes and natural language relations as edges, and is constructed by extracting (subject, relation, object) triples from text. The task of open-domain link prediction is to infer missing relations in the KG. Previous work has used standard link prediction for the task. Since triples are extracted from text, we can ground them in the larger textual context in which they were originally found. However, standard link prediction methods only rely on the KG structure and ignore the textual context that each triple was extracted from. In this paper, we introduce the new task of open-domain contextual link prediction which has access to both the textual context and the KG structure to perform link prediction. We build a dataset for the task and propose a model for it. Our experiments show that context is crucial in predicting missing relations. We also demonstrate the utility of contextual link prediction in discovering context-independent entailments between relations, in the form of entailment graphs (EG), in which the nodes are the relations. The reverse holds too: context-independent EGs assist in predicting relations in context.

2020

pdf bib
Incorporating Temporal Information in Entailment Graph Mining
Liane Guillou | Sander Bijl de Vroe | Mohammad Javad Hosseini | Mark Johnson | Mark Steedman
Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)

We present a novel method for injecting temporality into entailment graphs to address the problem of spurious entailments, which may arise from similar but temporally distinct events involving the same pair of entities. We focus on the sports domain in which the same pairs of teams play on different occasions, with different outcomes. We present an unsupervised model that aims to learn entailments such as win/lose → play, while avoiding the pitfall of learning non-entailments such as win ̸→ lose. We evaluate our model on a manually constructed dataset, showing that incorporating time intervals and applying a temporal window around them, are effective strategies.

2019

pdf bib
Duality of Link Prediction and Entailment Graph Induction
Mohammad Javad Hosseini | Shay B. Cohen | Mark Johnson | Mark Steedman
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Link prediction and entailment graph induction are often treated as different problems. In this paper, we show that these two problems are actually complementary. We train a link prediction model on a knowledge graph of assertions extracted from raw text. We propose an entailment score that exploits the new facts discovered by the link prediction model, and then form entailment graphs between relations. We further use the learned entailments to predict improved link prediction scores. Our results show that the two tasks can benefit from each other. The new entailment score outperforms prior state-of-the-art results on a standard entialment dataset and the new link prediction scores show improvements over the raw link prediction scores.

2018

pdf bib
Learning Typed Entailment Graphs with Global Soft Constraints
Mohammad Javad Hosseini | Nathanael Chambers | Siva Reddy | Xavier R. Holt | Shay B. Cohen | Mark Johnson | Mark Steedman
Transactions of the Association for Computational Linguistics, Volume 6

This paper presents a new method for learning typed entailment graphs from text. We extract predicate-argument structures from multiple-source news corpora, and compute local distributional similarity scores to learn entailments between predicates with typed arguments (e.g., person contracted disease). Previous work has used transitivity constraints to improve local decisions, but these constraints are intractable on large graphs. We instead propose a scalable method that learns globally consistent similarity scores based on new soft constraints that consider both the structures across typed entailment graphs and inside each graph. Learning takes only a few hours to run over 100K predicates and our results show large improvements over local similarity scores on two entailment data sets. We further show improvements over paraphrases and entailments from the Paraphrase Database, and prior state-of-the-art entailment graphs. We show that the entailment graphs improve performance in a downstream task.

2016

pdf bib
UW-CSE at SemEval-2016 Task 10: Detecting Multiword Expressions and Supersenses using Double-Chained Conditional Random Fields
Mohammad Javad Hosseini | Noah A. Smith | Su-In Lee
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2014

pdf bib
Learning to Solve Arithmetic Word Problems with Verb Categorization
Mohammad Javad Hosseini | Hannaneh Hajishirzi | Oren Etzioni | Nate Kushman
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)