Collaborating Authors

Reasoning Over Semantic-Level Graph for Fact Checking Artificial Intelligence

We study fact-checking in this paper, which aims to verify a textual claim given textual evidence (e.g., retrieved sentences from Wikipedia). Existing studies typically either concatenate retrieved sentences as a single string or use feature fusion on the top of features of sentences, while ignoring semantic-level information including participants, location, and temporality of an event occurred in a sentence and relationships among multiple events. Such semantic-level information is crucial for understanding the relational structure of evidence and the deep reasoning procedure over that. In this paper, we address this issue by proposing a graph-based reasoning framework, called the Dynamic REAsoning Machine (DREAM) framework. We first construct a semantic-level graph, where nodes are extracted by semantic role labeling toolkits and are connected by inner- and inter- sentence edges. After having the automatically constructed graph, we use XLNet as the backbone of our approach and propose a graph-based contextual word representation learning module and a graph-based reasoning module to leverage the information of graphs. The first module is designed by considering a claim as a sequence, in which case we use the graph structure to re-define the relative distance of words. On top of this, we propose the second module by considering both the claim and the evidence as graphs and use a graph neural network to capture the semantic relationship at a more abstract level. We conduct experiments on FEVER, a large-scale benchmark dataset for fact-checking. Results show that both of the graph-based modules improve performance. Our system is the state-of-the-art system on the public leaderboard in terms of both accuracy and FEVER score.

Multi-Hop Fact Checking of Political Claims Artificial Intelligence

Recently, novel multi-hop models and datasets have been introduced to achieve more complex natural language reasoning with neural networks. One notable task that requires multi-hop reasoning is fact checking, where a chain of connected evidence pieces leads to the final verdict of a claim. However, existing datasets do not provide annotations for the gold evidence pieces, which is a critical aspect for improving the explainability of fact checking systems. The only exception is the FEVER dataset, which is artificially constructed based on Wikipedia and does not use naturally occurring political claims and evidence pages, which is more challenging. Most claims in FEVER only have one evidence sentence associated with them and require no reasoning to make label predictions -- the small number of instances with two evidence sentences only require simple reasoning. In this paper, we study how to perform more complex claim verification on naturally occurring claims with multiple hops over evidence chunks. We first construct a small annotated dataset, PolitiHop, of reasoning chains for claim verification. We then compare the dataset to other existing multi-hop datasets and study how to transfer knowledge from more extensive in- and out-of-domain resources to PolitiHop. We find that the task is complex, and achieve the best performance using an architecture that specifically models reasoning over evidence chains in combination with in-domain transfer learning.

GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification Artificial Intelligence

Fact verification (FV) is a challenging task which requires to retrieve relevant evidence from plain text and use the evidence to verify given claims. Many claims require to simultaneously integrate and reason over several pieces of evidence for verification. However, previous work employs simple models to extract information from evidence without letting evidence communicate with each other, e.g., merely concatenate the evidence for processing. Therefore, these methods are unable to grasp sufficient relational and logical information among the evidence. To alleviate this issue, we propose a graph-based evidence aggregating and reasoning (GEAR) framework which enables information to transfer on a fully-connected evidence graph and then utilizes different aggregators to collect multi-evidence information. We further employ BERT, an effective pre-trained language representation model, to improve the performance. Experimental results on a large-scale benchmark dataset FEVER have demonstrated that GEAR could leverage multi-evidence information for FV and thus achieves the promising result with a test FEVER score of 67.10%. Our code is available at

TabFact: A Large-scale Dataset for Table-based Fact Verification Artificial Intelligence

The problem of verifying whether a textual hypothesis holds the truth based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation. However, existing studies are mainly restricted to dealing with unstructured evidence (e.g., natural language sentences and documents, news, etc), while verification under structured evidence, such as tables, graphs, and databases, remains unexplored. This paper specifically aims to study the fact verification given semi-structured data as evidence. To this end, we construct a large-scale dataset called \textsc{TabFact} with 16k Wikipedia tables as evidence for 118k human-annotated natural language statements, which are labeled as either {\tt ENTAILED} or {\tt REFUTED}. \textsc{TabFact} is more challenging since it involves both soft linguistic reasoning and hard symbolic reasoning. To address these reasoning challenges, we design two different models: Table-BERT and Latent Program Algorithm (LPA). Table-BERT leverages the state-of-the-art pre-trained language model to encode the linearized tables and statements into continuous vectors for verification. LPA parses statements into LISP-like programs and executes them against the tables to obtain the returned binary value. Both methods achieve similar accuracy but yet far from human performance. We also perform comprehensive analysis and demonstrate great future opportunities. The data and code of the dataset are provided in \url{}.

UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification Artificial Intelligence

The Fact Extraction and VERification (FEVER) shared task was launched to support the development of systems able to verify claims by extracting supporting or refuting facts from raw text. The shared task organizers provide a large-scale dataset for the consecutive steps involved in claim verification, in particular, document retrieval, fact extraction, and claim classification. In this paper, we present our claim verification pipeline approach, which, according to the preliminary results, scored third in the shared task, out of 23 competing systems. For the document retrieval, we implemented a new entity linking approach. In order to be able to rank candidate facts and classify a claim on the basis of several selected facts, we introduce two extensions to the Enhanced LSTM (ESIM).