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In adversarial data collection (ADC), a human workforce interacts with a model in real time, attempting to produce examples that elicit incorrect predictions. Researchers hope that models trained on these more challenging datasets will rely less on superficial patterns, and thus be less brittle. However, despite ADC&#39;s intuitive appeal, it remains unclear when training on adversarial datasets produces more robust models. In this paper, we conduct a large-scale controlled study focused on question answering, assigning workers at random to compose questions either (i) adversarially (with a model in the loop); or (ii) in the standard fashion (without a model). Across a variety of models and datasets, we find that models trained on adversarial data usually perform better 
on other adversarial datasets but worse on a diverse collection of out-of-domain evaluation sets. Finally, we provide a qualitative analysis of adversarial (vs standard) data, identifying key differences and offering guidance for future research.

IJCNLP-AACL 2021

On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study

out of domain generalization

adversarial data collection

question answering

**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.


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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).

In adversarial data collection (ADC), a human workforce interacts with a model in real time, attempting to produce examples that elicit incorrect predictions. Researchers hope that models trained on these more challenging datasets will rely less on superficial patterns, and thus be less brittle. However, despite ADC's intuitive appeal, it remains unclear when training on adversarial datasets produces more robust models. In this paper, we conduct a large-scale controlled study focused on question answering, assigning workers at random to compose questions either (i) adversarially (with a model in the loop); or (ii) in the standard fashion (without a model). Across a variety of models and datasets, we find that models trained on adversarial data usually perform better 
on other adversarial datasets but worse on a diverse collection of out-of-domain evaluation sets. Finally, we provide a qualitative analysis of adversarial (vs standard) data, identifying key differences and offering guidance for future research.

technical paper

Propaganda can be defined as a form of communication that aims to influence the opinions or the actions of people towards a specific goal; this is achieved by means of well-defined rhetorical and psychological devices. Propaganda, in the form we know it today, can be dated back to the beginning of the 17th century. However, it is with the advent of the Internet and the social media that propaganda has started to spread on a much larger scale than before, thus becoming major societal and political issue. Nowadays, a large fraction of propaganda in social media is multimodal, mixing textual with visual content. With this in mind, here we propose a new multi-label multimodal task: detecting the type of propaganda techniques used in memes. We further create and release a new corpus of 950 memes, carefully annotated with 22 propaganda techniques, which can appear in the text, in the image, or in both. Our analysis of the corpus shows that understanding both modalities together is essential for detecting these techniques. This is further confirmed in our experiments with several state-of-the-art multimodal models.

Detecting Propaganda Techniques in Memes

Learning prerequisite chains is an important task for one to pick up knowledge efficiently in both known and unknown domains. For example, one may be an expert in the natural language processing (NLP) domain, but want to determine the best order in which to learn new concepts in an unfamiliar Computer Vision domain (CV). Both domains share some common concepts, such as machine learning basics and deep learning models. In this paper, we solve the task of unsupervised cross-domain concept prerequisite chain learning, using an optimized variational graph autoencoder. Our model learns to transfer concept prerequisite relations from an information-rich domain (source domain) to an information-poor domain (target domain), substantially surpassing other baseline models. In addition, we expand an existing dataset by introducing two new domains––CV and Bioinformatics (BIO). The annotated data and resources as well as the code will be made publicly available.

Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoders

We present a text representation approach that can combine different views (representations) of the same input through effective data fusion and attention strategies for ranking purposes. We apply our model to the problem of differential diagnosis, which aims to find the most probable diseases that match with clinical descriptions of patients, using data from the Undiagnosed Diseases Network. Our model outperforms several ranking approaches (including a commercially-supported system) by effectively prioritizing and combining representations obtained from traditional and recent text representation techniques. We elaborate on several aspects of our model and shed light on its improved performance.

Attentive Multiview Text Representation for Differential Diagnosis

Crowdworker-constructed natural language inference (NLI) datasets have been found to contain statistical artifacts associated with the annotation process that allow hypothesis-only classifiers to achieve better-than-random performance~\citep{poliak-etal-2018-hypothesis, gururangan2018annotation,tsuchiya-2018-performance}. We investigate whether MedNLI, a physician-annotated dataset with premises extracted from clinical notes, contains such artifacts~\citep{romanov2018lessons}. 

We find that entailed hypotheses contain generic versions of specific concepts in the premise, as well as modifiers related to responsiveness, duration, and probability. Neutral hypotheses feature conditions and behaviors that co-occur with, or cause, the condition(s) in the premise. Contradiction hypotheses feature explicit negation of the premise and implicit negation via assertion of good health. Adversarial filtering demonstrates that performance degrades when evaluated on the \emph{difficult} subset. We provide partition information and recommendations for alternative dataset construction strategies for knowledge-intensive domains.

MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain

Writing a coherent and engaging story is not easy. Creative writers use their knowledge and worldview to put disjointed elements together to form a coherent storyline, and work and re-work iteratively toward perfection. Automated visual storytelling (VIST) models, however, make poor use of external knowledge and iterative generation when attempting to create stories. This paper introduces PR-VIST, a framework that represents the input image sequence as a story graph in which it finds the best path to form a storyline. PR-VIST then takes this path and learns to generate the final story via a re-evaluating training process. This framework produces stories that are superior in terms of diversity, coherence, and humanness, per both automatic and human evaluations. An ablation study shows that both plotting and reworking contribute to the model’s superiority.

Plot and Rework: Modeling Storylines for Visual Storytelling

A growing number of people engage in online health forums, making it important to understand the quality of the advice they receive. In this paper, we explore the role of expertise in responses provided to help-seeking posts regarding mental health. We study the differences between (1) interactions with peers; and (2) interactions with self-identified mental health professionals. First, we show that a classifier can distinguish between these two groups, indicating that their language use does in fact differ. To understand this difference, we perform several analyses addressing engagement aspects, including whether their comments engage the support-seeker further as well as linguistic aspects, such as dominant language and linguistic style matching. Our work contributes toward the developing efforts of understanding how health experts engage with health information- and support-seekers in social networks. More broadly, it is a step toward a deeper understanding of the styles of interactions that cultivate supportive engagement in online communities.

Exploring Self-Identified Counseling Expertise in Online Support Forums

Learning a good latent representation is essential for text style transfer, which generates a new sentence by changing the attributes of a given sentence while preserving its content. Most previous work adopt disentangled latent representation learning to realize style transfer. We propose a novel text style transfer algorithm with entangled latent representation, and introduce a style classifier that can regulate the latent structure and transfer style. Moreover, our algorithm for style transfer applies to both single-attribute and multi-attribute transfer. Extensive experimental results show that our method generally outperforms state-of-the-art approaches.

Text Style Transfer: Leveraging a Style Classifier on Entangled Latent Representations

Conventional Knowledge Graph Completion (KGC) assumes that all test entities appear during training. However, in real-world scenarios, Knowledge Graphs (KG) evolve fast with out-of-knowledge-graph (OOKG) entities added frequently, and we need to efficiently represent these entities. Most existing Knowledge Graph Embedding (KGE) methods cannot represent OOKG entities without costly retraining on the whole KG. To enhance efficiency, we propose a simple and effective method that inductively represents OOKG entities by their optimal estimation under translational assumptions. Moreover, given pretrained embeddings of the in-knowledge-graph (IKG) entities, our method even needs no additional learning. Experimental results on two KGC tasks with OOKG entities show that our method outperforms the previous methods by a large margin with higher efficiency.

Inductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational Assumptions

We explore task-adaptive pretraining (TAPT) with adapters and show that directly fine-tuning adapters can achieve comparable performance to TAPT while reducing training time by skipping the second phase of pretraining.

Revisiting Pretraining with Adapters

Typically, methods for improving weakly supervised learning are tailored towards domain-specific tasks or integrated with a specific model architecture. That makes them hard to be compared or transferred across tasks without extensive adjustments. We introduce Knodle: a framework that provides accessibility, data abstraction, and model independence. It allows easy experimentations and benchmarking of different denoising algorithms, tasks, and datasets. 
Knodle is published as an open-source Python package knodle and also available at https://github.com/knodle/knodle.

On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study

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Detecting Propaganda Techniques in Memes

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On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study

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Next from IJCNLP-AACL 2021

Detecting Propaganda Techniques in Memes

Similar lecture

Knowing More About Questions Can Help: Improving Calibration in Question Answering

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads