Thailand

While social media becomes a primary source of news now, it also becomes more challenging for people to distinguish the rumors and non-rumors, which attracts malicious manipulation and may lead to public health harm or economic loss. Consequently, many rumor detection models have been proposed to automatically detect the rumors based on the contents and propagation path. However, most previous works are not aware of malicious attacks, e.g., framing. Therefore, 
we propose a novel rumor detection framework, Adversary-Aware Rumor Detection including Weighted-Edge Transformer-Graph Network and Position-aware Adversarial Response Generator, to improve the vulnerability of detection models. To the best of our knowledge, this is the first work that can generate the adversarial response with the consideration of the response position. Experimental results show that our model achieves the state-of-the-art on various rumor detection tasks by the proposed Weighted-Edge Transformer-Graph Network and can maintain the performance under the adversarial response attack after the adversarial learning by Position-aware Adversarial Response Generator.

ACL-IJCNLP 2021

Adversary-Aware Rumor Detection

adversarial example

rumor detection

response generation

graph neural network

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

technical paper

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

NLP has a rich history of representing our prior understanding of language in the form of graphs. Recent work on analyzing contextualized text representations has focused on hand-designed probe models to understand how and to what extent do these representations encode a particular linguistic phenomenon. However, due to the inter-dependence of various phenomena and randomness of training probe models, detecting how these representations encode the rich information in these linguistic graphs remains a challenging problem. In this paper, we propose a new information-theoretic probe, Bird's Eye, which is a fairly simple probe method for detecting if and how these representations encode the information in these linguistic graphs. Instead of using model performance, our probe takes an information-theoretic view of probing and estimates the mutual information between the linguistic graph embedded in a continuous space and the contextualized word representations. Furthermore, we also propose an approach to use our probe to investigate localized linguistic information in the linguistic graphs using perturbation analysis. We call this probing setup Worm's Eye. Using these probes, we analyze the BERT models on its ability to encode a syntactic and a semantic graph structure, and find that these models encode to some degree both syntactic as well as semantic information; albeit syntactic information to a greater extent.

Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach

Previous literatures show that pre-trained masked language models (MLMs) such as BERT can achieve competitive factual knowledge extraction performance on some datasets, indicating that MLMs can potentially be a reliable knowledge source. In this paper, we conduct a rigorous study to explore the underlying predicting mechanisms of MLMs over different extraction paradigms. By investigating the behaviors of MLMs, we find that previous decent performance mainly owes to the biased prompts which overfit dataset artifacts. Furthermore, incorporating illustrative cases and external contexts improve knowledge prediction mainly due to entity type guidance and golden answer leakage. Our findings shed light on the underlying predicting mechanisms of MLMs, and strongly question the previous conclusion that current MLMs can potentially serve as reliable factual knowledge bases.

Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases

We study the problem of generating data poisoning attacks against Knowledge Graph Embedding (KGE) models for the task of link prediction in knowledge graphs. To poison KGE models, we propose to exploit their inductive abilities which are captured through the relationship patterns like symmetry, inversion and composition in the knowledge graph. Specifically, to degrade the model's prediction confidence on target facts, we propose to improve the model's prediction confidence on a set of decoy facts. Thus, we craft adversarial additions that can improve the model's prediction confidence on decoy facts through different inference patterns. Our experiments demonstrate that the proposed poisoning attacks outperform state-of-art baselines on four KGE models for two publicly available datasets. We also find that the symmetry pattern based attacks generalize across all model-dataset combinations which indicates the sensitivity of KGE models to this pattern.

Poisoning Knowledge Graph Embeddings via Relation Inference Patterns

Pre-trained language models have achieved human-level performance on many Machine Reading Comprehension (MRC) tasks, but it remains unclear whether these models truly understand language or answer questions by exploiting statistical biases in datasets. Here, we demonstrate a simple yet effective method to attack MRC models and reveal the statistical biases in these models. We apply the method to the RACE dataset, for which the answer to each MRC question is selected from 4 options. It is found that several pre-trained language models, including BERT, ALBERT, and RoBERTa, show consistent preference to some options, even when these options are irrelevant to the question. When interfered by these irrelevant options, the performance of MRC models can be reduced from human-level performance to the chance-level performance. Human readers, however, are not clearly affected by these irrelevant options. Finally, we propose an augmented training method that can greatly reduce models' statistical biases.

Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models

A common factor in bias measurement methods is the use of hand-curated seed lexicons, but there remains little guidance for their selection. We gather seeds used in prior work, documenting their common sources and rationales, and in case studies of three English-language corpora, we enumerate the different types of social biases and linguistic features that, once encoded in the seeds, can affect subsequent bias measurements. Seeds developed in one context are often re-used in other contexts, but documentation and evaluation remain necessary precursors to relying on seeds for sensitive measurements.

Bad Seeds: Evaluating Lexical Methods for Bias Measurement

Despite inextricable ties between race and language, little work has considered race in NLP research and development. In this work, we survey 79 papers from the ACL anthology that mention race. These papers reveal various types of race-related bias in all stages of NLP model development, highlighting the need for proactive consideration of how NLP systems can uphold racial hierarchies. However, persistent gaps in research on race and NLP remain: race has been siloed as a niche topic and remains ignored in many NLP tasks; most work operationalizes race as a fixed single-dimensional variable with a ground-truth label, which risks reinforcing differences produced by historical racism; and the voices of historically marginalized people are nearly absent in NLP literature. By identifying where and how NLP literature has and has not considered race, especially in comparison to related fields, our work calls for inclusion and racial justice in NLP research practices.

A Survey of Race, Racism, and Anti-Racism in NLP

Natural Language Processing (NLP) systems learn harmful societal biases that cause them to amplify inequality as they are deployed in more and more situations. To guide efforts at debiasing these systems, the NLP community relies on a variety of metrics that quantify bias in models. Some of these metrics are intrinsic, measuring bias in word embedding spaces, and some are extrinsic, measuring bias in downstream tasks that the word embeddings enable. Do these intrinsic and extrinsic metrics correlate with each other? We compare intrinsic and extrinsic metrics across hundreds of trained models covering different tasks and experimental conditions. Our results show no reliable correlation between these metrics that holds in all scenarios across tasks and languages. We urge researchers working on debiasing to focus on extrinsic measures of bias, and to make using these measures more feasible via creation of new challenge sets and annotated test data. To aid this effort, we release code, a new intrinsic metric, and an annotated test set focused on gender bias in hate speech.

Intrinsic Bias Metrics Do Not Correlate with Application Bias

Recently, driven by numerous publicly available machine reading comprehension (MRC) datasets, MRC systems have made some progress. These datasets, however, have two major limitations: 1) the defined tasks are relatively simple, and 2) they do not provide explainable evaluation which is critical to objectively and comprehensively review the reasoning capabilities of current MRC systems. In this paper, we propose GCRC, a new dataset with challenging and high-quality multi-choice questions, collected from Gaokao Chinese (Chinese subject from the National College Entrance Examination of China). We have manually labelled three types of evidence to evaluate MRC systems’ reasoning process: 1) sentence-level relevant supporting facts in an article required for answering a given question, 2) error reason of a distractor (i.e., an incorrect option) for explaining why a distractor should be eliminated, which is an important reasoning step for multi-choice questions, and 3) types of reasoning skills required for answering questions. Extensive experiments show that our proposed dataset is more challenging and very useful for identifying the limitations of existing MRC systems in an explainable way, facilitating researchers to develop novel machine learning and reasoning approaches to tackle this challenging research problem.

GCRC: A New Challenging MRC Dataset from Gaokao Chinese for Explainable Evaluation

Identifying events and mapping them to a pre-defined taxonomy of event types has long been an important NLP problem. Most previous work has relied heavily on labor-intensive, domain-specific, annotation, ignoring the {\em semantic meaning} of the event types’ labels. Consequently, the learned models cannot effectively generalize to new label taxonomies and domains. We propose a zero-shot event extraction approach, which first identifies events with existing tools (e.g., SRL) and then maps them to a given taxonomy of event types in a zero-shot manner. Specifically, we leverage label representations induced by pre-trained language models, and map identified events to the target types via representation similarity. To semantically type the events’ arguments, we further use the definition of the events (e.g., argument of type ``Victim'' appears as the argument of event of type ``Attack'') as global constraints to regularize the prediction. The proposed approach is shown to be very effective on the ACE-2005 dataset, which has 33 trigger and 22 argument types. Without using any annotation, we successfully map 83% of the triggers and 54% of the arguments to the semantic correct types, almost doubling the performance of previous zero-shot approaches.

{Z}ero-shot {L}abel-Aware {E}vent {T}rigger and {A}rgument {C}lassification

Aspect sentiment classification (ASC) aims at determining sentiments expressed towards different aspects in a sentence. While state-of-the-art ASC models have achieved remarkable performance, they are recently shown to suffer from the issue of robustness. Particularly in two common scenarios: when domains of test and training data are different (out-of-domain scenario) or test data is adversarially perturbed (adversarial scenario), ASC models may attend to irrelevant words and neglect opinion expressions that truly describe diverse aspects. To tackle the challenge, in this paper, we hypothesize that position bias (i.e., the words closer to a concerning aspect would carry a higher degree of importance) is crucial for building more robust ASC models by reducing the probability of mis-attending. Accordingly, we propose two mechanisms for capturing position bias, namely position-biased weight and position-biased dropout, which can be flexibly injected into existing models to enhance representations for classification. Experiments conducted on out-of-domain and adversarial datasets demonstrate that our proposed approaches largely improve the robustness and effectiveness of current models.

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