Search Results for author: Mohammad Javad Hosseini

Found 17 papers, 10 papers with code

Learning Typed Entailment Graphs with Global Soft Constraints

1 code implementation TACL 2018 Mohammad Javad Hosseini, Nathanael Chambers, Siva Reddy, Xavier R. Holt, Shay B. Cohen, Mark Johnson, Mark Steedman

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.

Graph Learning

Duality of Link Prediction and Entailment Graph Induction

1 code implementation ACL 2019 Mohammad Javad Hosseini, Shay B. Cohen, Mark Johnson, Mark Steedman

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.

Link Prediction

Jointly Modeling Hierarchical and Horizontal Features for Relational Triple Extraction

no code implementations23 Aug 2019 Zhepei Wei, Yantao Jia, Yuan Tian, Mohammad Javad Hosseini, Sujian Li, Mark Steedman, Yi Chang

In this work, we first introduce the hierarchical dependency and horizontal commonality between the two levels, and then propose an entity-enhanced dual tagging framework that enables the triple extraction (TE) task to utilize such interactions with self-learned entity features through an auxiliary entity extraction (EE) task, without breaking the joint decoding of relational triples.

Entity Extraction using GAN graph construction +2

Spatial Language Representation with Multi-Level Geocoding

1 code implementation21 Aug 2020 Sayali Kulkarni, Shailee Jain, Mohammad Javad Hosseini, Jason Baldridge, Eugene Ie, Li Zhang

We present a multi-level geocoding model (MLG) that learns to associate texts to geographic locations.

Toponym Resolution

Incorporating Temporal Information in Entailment Graph Mining

1 code implementation COLING (TextGraphs) 2020 Liane Guillou, Sander Bijl de Vroe, Mohammad Javad Hosseini, Mark Johnson, Mark Steedman

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.

Graph Mining

Cross-lingual Inference with A Chinese Entailment Graph

1 code implementation Findings (ACL) 2022 Tianyi Li, Sabine Weber, Mohammad Javad Hosseini, Liane Guillou, Mark Steedman

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.

Entity Typing Question Answering +2

Language Models Are Poor Learners of Directional Inference

1 code implementation10 Oct 2022 Tianyi Li, Mohammad Javad Hosseini, Sabine Weber, Mark Steedman

We examine LMs' competence of directional predicate entailments by supervised fine-tuning with prompts.

Resolving Indirect Referring Expressions for Entity Selection

1 code implementation21 Dec 2022 Mohammad Javad Hosseini, Filip Radlinski, Silvia Pareti, Annie Louis

We address this problem of reference resolution, when people use natural expressions to choose between the entities.

Language Modelling

Sources of Hallucination by Large Language Models on Inference Tasks

1 code implementation23 May 2023 Nick McKenna, Tianyi Li, Liang Cheng, Mohammad Javad Hosseini, Mark Johnson, Mark Steedman

Large Language Models (LLMs) are claimed to be capable of Natural Language Inference (NLI), necessary for applied tasks like question answering and summarization.

Hallucination Memorization +2

LAIT: Efficient Multi-Segment Encoding in Transformers with Layer-Adjustable Interaction

no code implementations31 May 2023 Jeremiah Milbauer, Annie Louis, Mohammad Javad Hosseini, Alex Fabrikant, Donald Metzler, Tal Schuster

Transformer encoders contextualize token representations by attending to all other tokens at each layer, leading to quadratic increase in compute effort with the input length.

A synthetic data approach for domain generalization of NLI models

no code implementations19 Feb 2024 Mohammad Javad Hosseini, Andrey Petrov, Alex Fabrikant, Annie Louis

We also demonstrate gains on test sets when in-domain training data is augmented with our domain-general synthetic data.

Domain Generalization Natural Language Inference +1

Open-Domain Contextual Link Prediction and its Complementarity with Entailment Graphs

1 code implementation Findings (EMNLP) 2021 Mohammad Javad Hosseini, Shay B. Cohen, Mark Johnson, Mark Steedman

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.

Link Prediction

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