Croatia

The research carried out so far in detecting abusive content in social media has primarily focused on overt forms of hate speech. While explicit hate speech (HS) is more easily identifiable by recognizing hateful words, messages containing linguistically subtle and implicit forms of HS (as circumlocution, metaphors and sarcasm) constitute a real challenge for automatic systems. While the sneaky and tricky nature of subtle messages might be perceived as less hurtful with respect to the same content expressed clearly, such abuse is at least as harmful as overt abuse. In this paper, we first provide an in-depth and systematic analysis of 7 standard benchmarks for HS detection, relying on a fine-grained and linguistically-grounded definition of implicit and subtle messages. Then, we experiment with state-of-the-art neural network architectures on two supervised tasks, namely implicit HS and subtle HS message classification. We show that while such models perform satisfactory on explicit messages, they fail to detect implicit and subtle content, highlighting the fact that HS detection is not a solved problem and deserves further investigation.

EACL 2023

An In-depth Analysis of Implicit and Subtle Hate Speech Messages

implicit language

hate speech

social media

poster

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

In sensitive domains, the sharing of corpora is restricted due to confidentiality, copyrights or trade secrets. Automatic text generation can help alleviate these issues by producing synthetic texts that mimic the linguistic properties of real documents while preserving confidentiality. In this study, we assess the usability of synthetic corpus as a substitute training corpus for clinical information extraction. Our goal is to automatically produce a clinical case corpus annotated with clinical entities and to evaluate it for a named entity recognition (NER) task. 
We use two auto-regressive neural models partially or fully trained on generic French texts and fine-tuned on clinical cases to produce a corpus of synthetic clinical cases. We study variants of the generation process: (i) fine-tuning on annotated vs. plain text (in that case, annotations are obtained a posteriori) and (ii) selection of generated texts based on models parameters and filtering criteria. We then train NER models with the resulting synthetic text and evaluate them on a gold standard clinical corpus. 
Our experiments suggest that synthetic text is useful for clinical NER.

Can Synthetic Text Help Clinical Named Entity Recognition? A Study of Electronic Health Records in French

Character-level language modeling has been shown empirically to perform well on highly agglutinative or morphologically rich languages while using only a small fraction of the parameters required by (sub)word models. Korean fits nicely into this framework, except that, like other CJK languages, it has a very large character vocabulary of 11,172 unique syllables. However, unlike Japanese Kanji and Chinese Hanzi, each Korean syllable can be uniquely factored into a small set of subcharacters, called jamo.

We explore a "three-hot" scheme, where we exploit the decomposability of Korean characters to model at the syllable level but using only jamo-level representations.

We find that our three-hot embedding and decoding scheme alleviates the two major issues with prior syllable- and jamo-level models. Namely, it requires fewer than 1\% of the embedding parameters of a syllable model, and it does not require tripling the sequence length, as with jamo models. In addition, it addresses a theoretical flaw in a prior three-hot modeling scheme.

Our experiments show that, even when reducing the number of embedding parameters by \textgreater{}99.6\% (from 11.4M to just 36k), our model suffers no loss in translation quality compared to the baseline syllable model.

Parameter-Efficient Korean Character-Level Language Modeling

Discrete prompts have been used for fine-tuning Pre-trained Language Models for diverse NLP tasks. In particular, automatic methods that generate discrete prompts from a small set of training instances have reported superior performance. However, a closer look at the learnt prompts reveals that they contain noisy and counter-intuitive lexical constructs that would not be encountered in manually-written prompts. This raises an important yet understudied question regarding the robustness of automatically learnt discrete prompts when used in downstream tasks. To address this question, we conduct a systematic study of the robustness of discrete prompts by applying carefully designed perturbations into an application using AutoPrompt and then measure their performance in two Natural Language Inference (NLI) datasets. Our experimental results show that although the discrete prompt-based method remains relatively robust against perturbations to NLI inputs, they are highly sensitive to other types of perturbations such as shuffling and deletion of prompt tokens. Moreover, they generalize poorly across different NLI datasets. We hope our findings will inspire future work on robust discrete prompt learning.

Evaluating the Robustness of Discrete Prompts

Advancements in Neural Machine Translation (NMT) greatly benefit the software localization industry by decreasing the post-editing time of human annotators. Although the volume of the software being localized is growing significantly, techniques for improving NMT for user interface (UI) texts are lacking. These UI texts have different properties than other collections of texts, presenting unique challenges for NMT. For example, they are often very short, causing them to be ambiguous and needing additional context (button, title text, a table item, etc.) for disambiguation. However, no such UI data sets are readily available with contextual information for NMT models to exploit. This work aims to provide a first step in improving UI translations and highlight its challenges. To achieve this, we provide a novel multilingual UI corpus collection (\$\textbackslash{}sim1.3M\$ for English \$\textbackslash{}leftrightarrow\$ German) with a targeted test set and analyze the limitations of state-of-the-art methods on this challenging task. Specifically, we present a targeted test set for disambiguation from English to German to evaluate reliably and emphasize UI translation challenges. Furthermore, we evaluate several state-of-the-art NMT techniques from domain adaptation and document-level NMT on this challenging task. All the scripts to replicate the experiments and data sets are available here.\textbackslash{}footnote\{\textbackslash{}url\{https://github.com/saikoneru/NMT\textbackslash{}\_Localization\}\}\$\^{}\{,\}\$\textbackslash{}footnote\{We crawled this data only for scientific research.\}

Analyzing Challenges in Neural Machine Translation for Software Localization

Despite cross-lingual generalization demonstrated by pre-trained multilingual models, the translate-train paradigm of transferring English datasets across multiple languages remains to be a key mechanism for training task-specific multilingual models. However, for many low-resource languages, the availability of a reliable translation service entails significant amounts of costly human-annotated translation pairs. Further, translation services may continue to be brittle due to domain mismatch between task-specific input text and general-purpose text used for training translation models. For multilingual semantic parsing, we demonstrate the effectiveness and flexibility offered by large language models (LLMs) for translating English datasets into several languages via few-shot prompting. Through extensive comparisons on two public datasets, MTOP and MASSIVE, spanning 50 languages and several domains, we show that our method of translating data using LLMs outperforms a strong translate-train baseline on 41 out of 50 languages. We study the key design choices that enable more effective multilingual data translation via prompted LLMs.

Bootstrapping Multilingual Semantic Parsers using Large Language Models

In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions in a general purpose knowledge graph (KG) with very large vocabularies (covering thousands of concept names and relations, and millions of entities). To this end, we develop a dataset where user questions are annotated with Sparql parses and system answers correspond to execution results thereof. We present two different semantic parsing approaches and highlight the challenges of the task: dealing with large vocabularies, modelling conversation context, predicting queries with multiple entities, and generalising to new questions at test time. We hope our dataset will serve as useful testbed for the development of conversational semantic parsers. Our dataset and models are released at https://github.com/EdinburghNLP/SPICE.

Semantic Parsing for Conversational Question Answering over Knowledge Graphs

Current state-of-the-art coreference systems are based on a single pairwise scoring component, which assigns to each pair of mention spans a score reflecting their tendency to corefer to each other. We observe that different kinds of mention pairs require different information sources to assess their score. We present LingMess, a linguistically motivated categorization of mention-pairs into 6 types of coreference decisions and learn a dedicated trainable scoring function for each category. This significantly improves the accuracy of the pairwise scorer as well as of the overall coreference performance on the English Ontonotes coreference corpus and 5 additional datasets.

LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution

Statutory article retrieval (SAR), the task of retrieving statute law articles relevant to a legal question, is a promising application of legal text processing. In particular, high-quality SAR systems can improve the work efficiency of legal professionals and provide basic legal assistance to citizens in need at no cost. Unlike traditional ad-hoc information retrieval, where each document is considered a complete source of information, SAR deals with texts whose full sense depends on complementary information from the topological organization of statute law. While existing works ignore these domain-specific dependencies, we propose a novel graph-augmented dense statute retriever (G-DSR) model that incorporates the structure of legislation via a graph neural network to improve dense retrieval performance. Experimental results show that our approach outperforms strong retrieval baselines on a real-world expert-annotated SAR dataset.

Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks

Numerous types of social biases have been identified in pre-trained language models (PLMs), and various intrinsic bias evaluation measures have been proposed for quantifying those social biases.
Prior works have relied on human annotated examples to compare existing intrinsic bias evaluation measures.
However, this approach is not easily adaptable to different languages nor amenable to large scale evaluations due to the costs and difficulties when recruiting human annotators.
To overcome this limitation, we propose a method to compare intrinsic gender bias evaluation measures without relying on human-annotated examples.
Specifically, we create multiple bias-controlled versions of PLMs using varying amounts of male vs. female gendered sentences, mined automatically from an unannotated corpus using gender-related word lists. 
Next, each bias-controlled PLM is evaluated using an intrinsic bias evaluation measure, and the rank correlation between the computed bias scores and the gender proportions used to fine-tune the PLMs is computed.
Experiments on multiple corpora and PLMs repeatedly show that the correlations reported by our proposed method that does not require human annotated examples are comparable to those computed using human annotated examples in prior work.

Comparing Intrinsic Gender Bias Evaluation Measures without using Human Annotated Examples

Masked language models (MLMs) conventionally mask 15\% of tokens due to the belief that more masking would leave insufficient context to learn good representations; this masking rate has been widely used, regardless of model sizes or masking strategies. In this work, we revisit this important choice of MLM pre-training. We first establish that 15\% is not universally optimal, and larger models should adopt a higher masking rate. Specifically, we find that masking 40\% outperforms 15\% for BERT-large size models on GLUE and SQuAD. Interestingly, an extremely high masking rate of 80\% can still preserve 95\% fine-tuning performance and most of the accuracy in linguistic probing, challenging the conventional wisdom about the role of the masking rate. We then examine the interplay between masking rates and masking strategies and find that uniform masking requires a higher masking rate compared to sophisticated masking strategies such as span or PMI masking. Finally, we argue that increasing the masking rate has two distinct effects: it leads to more corruption, which makes the prediction task more difficult; it also enables more predictions, which benefits optimization. Using this framework, we revisit BERT's 80-10-10 corruption strategy. Together, our results contribute to a better understanding of MLM pre-training.

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Can Synthetic Text Help Clinical Named Entity Recognition? A Study of Electronic Health Records in French