Thailand

The Shared Task on Hateful Memes is a challenge that aims at the detection of hateful content in memes by inviting the implementation of systems that understand memes, potentially by combining image and textual information. The challenge consists of three detection tasks: hate, protected category and attack type. The first is a binary classification task, while the other two are multi-label classification tasks. Our participation included a text-based BERT baseline (TxtBERT), the same but adding information from the image (ImgTxtBERT), and neural retrieval approaches. We also experimented with retrieval augmented classification models. We find that an ensemble of TxtBERT and ImgBERT achieves the best performance in two out of the three tasks.

IJCNLP-AACL 2021

Multimodal or Text? Retrieval or BERT? Benchmarking Classifiers for the Shared Task on Hateful Memes

multimodal methods

retrieval

text classification

**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).

**Reminder:** It is ACL’s policy that at least one author of each accepted paper (including ACL Finding papers) must register for the conference.

**Reminder2:** Underline site will open closer to the event. If you already registered you will receive access detail

Registration is now open

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

workshop paper

Open relation extraction aims to cluster relation instances referring to the same underlying relation, which is a critical step for general relation extraction. Current OpenRE models are commonly trained on the datasets generated from distant supervision, which often results in instability and makes the model easily collapsed. In this paper, we revisit the procedure of OpenRE from a causal view. By formulating OpenRE using a structural causal model, we identify that the above-mentioned problems stem from the spurious correlations from entities and context to the relation type. To address this issue, we conduct \emph{Element Intervention}, which intervene on the context and entities respectively to obtain the underlying causal effects of them. We also provide two specific implementations of the interventions based on entity ranking and context contrasting. Experimental results on unsupervised relation extraction datasets show our method to outperform previous state-of-the-art methods and is robust across different datasets.

Element Intervention for Open Relation Extraction

With the rapid development of NLP research, leaderboards have emerged as one tool to track the performance of various systems on various NLP tasks. They are effective in this goal to some extent, but generally present a rather simplistic one-dimensional view of the submitted systems, communicated only through holistic accuracy numbers. In this paper, we present a new conceptualization and implementation of NLP evaluation: the \textsc{ExplainaBoard}, which in addition to inheriting the functionality of the standard leaderboard, also allows researchers to (i) diagnose strengths and weaknesses of a single system (e.g.~what is the best-performing system bad at?) (ii) interpret relationships between multiple systems. (e.g.~where does system \texttt{A} outperform system \texttt{B}? What if we combine systems \texttt{A}, \texttt{B} and \texttt{C}?) and (iii) examine prediction results closely (e.g.~what are common errors made by multiple systems, or in what contexts do particular errors occur?). 
So far, \textsc{ExplainaBoard} covers more than 400 systems, 50 datasets, 40 languages, and 12 tasks.
We not only released an online \textbf{platform} 
but also make our evaluation tool an \textbf{API} with \textit{MIT Licence} at Github
and PyPi that allows users to conveniently assess their models offline. We additionally release all \textbf{output files} from systems that we have run or collected to motivate ``output-driven'' research in the future.


ExplainaBoard: An Explainable Leaderboard for NLP

Automated Essay Assessment (AEA) aims to judge students' writing proficiency in an automatic way. This paper presents a Chinese AEA system IFlyEssayAssess (IFlyEA), targeting on evaluating essays written by native Chinese students from primary and junior schools. IFlyEA provides multi-level and multi-dimension analytical modules for essay assessment. It has state-of-the-art grammar level analysis techniques, and also integrates components for rhetoric and discourse level analysis, which are important for evaluating native speakers' writing ability, but still challenging and less studied in previous work. Based on the comprehensive analysis, IFlyEA provides application services for essay scoring, review generation, recommendation, and explainable analytical visualization. These services can benefit both teachers and students during the process of writing teaching and learning.

IFlyEA: A Chinese Essay Assessment System with Automated Rating

We present TweeNLP, a one-stop portal that organizes Twitter’s natural language processing (NLP) data and builds a visualization and exploration platform. It curates 19,395 tweets (as of April 2021) from various NLP conferences and general NLP discussions. It supports multiple features such as TweetExplorer to explore tweets by topics, visualize insights from Twitter activity throughout the organization cycle of conferences, discover popular research papers and researchers. It also builds a timeline of conference and workshop submission deadlines. We envision TweeNLP to function as a collective memory unit for the NLP community by integrating the tweets pertaining to research papers with the NLPExplorer scientific literature search engine. The current system is hosted at http://nlpexplorer.org/twitter/CFP.

TweeNLP: A Twitter Exploration Portal for Natural Language Processing

Content moderation is often performed by a collaboration between humans and machine learning models. However, it is not well understood how to design the collaborative process so as to maximize the combined moderator-model system performance. This work presents a rigorous study of this problem, focusing on an approach that incorporates model uncertainty into the collaborative process. First, we introduce principled metrics to describe the performance of the collaborative system under capacity constraints on the human moderator, quantifying how efficiently the combined system utilizes human decisions. Using these metrics, we conduct a large benchmark study evaluating the performance of state-of-the-art uncertainty models under different collaborative review strategies. We find that an uncertainty-based strategy consistently outperforms the widely used strategy based on toxicity scores, and moreover that the choice of review strategy drastically changes the overall system performance. Our results demonstrate the importance of rigorous metrics for understanding and developing effective moderator-model systems for content moderation, as well as the utility of uncertainty estimation in this domain.

Measuring and Improving Model-Moderator Collaboration using Uncertainty Estimation

This presentation is about our submission (winning solution) to the Shared Task on Hateful Memes Detection at WOAH 2021 (Task A). 
We built our system on top of the winning system of a previous hateful memes binary classification shared task and used further metadata information such as emotions extracted from facial expressions to the already proposed image tags such as race, gender, and web entities.
Furthermore, we experiment with data augmentation techniques, as class imbalance is still an issue in this task.

VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes

Memes are the combinations of text and images that are often humorous in nature. But, that may not always be the case and certain combinations of texts and images may depict hate, referred to as hateful memes. This work presents a multimodal pipeline that takes both visual and textual features from memes into account to (1) identify the protected category (e.g. race, sex etc.) that has been attacked and (2) detect the type of attack (e.g. contempt, slurs etc.). Our pipeline utilizes state-of-the-art pre-trained visual and textual representations, followed by a simple logistic regression classifier. We employ our pipeline on the Hateful Memes Challenge dataset with additional newly created fine-grained labels for protected category and type of attack. Our best model achieves an AUROC of 0.96 for identifying protected category, and 0.97 for detecting the type of attack. We release our code at https://github.com/harisbinzia/HatefulMemes.

Multimodal or Text? Retrieval or BERT? Benchmarking Classifiers for the Shared Task on Hateful Memes

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