United Arab Emirates

In hate speech detection, developing training and evaluation datasets across various domains is the critical issue. Whereas, major approaches crawl social media texts and hire crowd-workers to annotate the data. Following this convention often restricts the scope of pejorative expressions to a single domain lacking generalization. Sometimes domain overlap between training corpus and evaluation set overestimate the prediction performance when pretraining language models on low-data language. To alleviate these problems in Korean, we propose APEACH that asks unspecified users to generate hate speech examples followed by minimal post-labeling. We find that APEACH can collect useful datasets that are less sensitive to the lexical overlaps between the pretraining corpus and the evaluation set, thereby properly measuring the model performance.

EMNLP 2022

APEACH: Attacking Pejorative Expressions with Analysis on Crowd-Generated Hate Speech Evaluation Datasets

hate-speech

datasets

evaluation

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
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ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

findings / work in progress

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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

Automated storytelling has long captured the attention of researchers for the ubiquity of narratives in everyday life. However, it is challenging to maintain coherence and stay on-topic
toward a specific ending when generating narratives with neural language models. In this paper, we introduce Story generation with Reader
Models (StoRM), a framework in which a
reader model is used to reason about the story
should progress. A reader model infers what
a human reader believes about the concepts,
entities, and relations about the fictional story
world. We show how an explicit reader model
represented as a knowledge graph affords the story
coherence and provides controllability in the
form of achieving a given story world state
goal. Experiments show that our model produces significantly more coherent and on-topic
stories, outperforming baselines in dimensions
including plot plausibility and staying on topic

Guiding Neural Story Generation with Reader Models

The scarcity of gold standard code-mixed to pure language parallel data makes it difficult to train translation models reliably.
Prior work has addressed the paucity of parallel data with data augmentation techniques.
Such methods rely heavily on external resources making systems difficult to train and scale effectively for multiple languages.
We present a simple yet highly effective two-stage back-translation based training scheme for adapting multilingual models to the task of code-mixed translation which eliminates dependence on external resources.
We show a substantial improvement in translation quality (measured through BLEU), beating existing prior work by up to +3.8 BLEU on code-mixed Hi$\rightarrow$En, Mr$\rightarrow$En, and Bn$\rightarrow$En tasks. On the LinCE Machine Translation leader board, we achieve the highest score for code-mixed Es$\rightarrow$En, beating existing best baseline by +6.5 BLEU, and our own stronger baseline by +1.1 BLEU.

Adapting Multilingual Models for Code-Mixed Translation

When we execute the typical fine-tuning paradigm on continuously sequential tasks, the model will suffer from the catastrophic forgetting problem (i.e., the model tends to adjust old parameters according to the new knowledge, which leads to the loss of previously acquired concepts). People proposed replay-based methods by accessing old data from extra storage and maintaining the parameters of old concepts, which actually raise the privacy issue and larger memory requirements. In this work, we aim to achieve the sequential/continual learning of knowledge without accessing the old data. The core idea is to calibrate the parameters and logits (output) so that preserving old parameters and generalized learning on new concepts can be solved simultaneously. Our proposed framework includes two major components, Logits Calibration (LC) and Parameter Calibration (PC). The LC focuses on calibrating the learning of novel models with old models, and PC aims to preserve the parameters of old models. These two operations can maintain the old knowledge while learning new tasks without storing previous data. We conduct experiments on various scenarios of the GLUE (the General Language Understanding Evaluation) benchmark. The experimental results show that our model achieves state-of-the-art performance in all scenarios.

LPC: A Logits and Parameter Calibration Framework for Continual Learning

Event extraction (EE) is the task of identifying interested event mentions from text.
Conventional efforts mainly focus on the supervised setting. However, these supervised models cannot generalize to event types out of the pre-defined ontology. To fill this gap, many efforts have been devoted to the zero-shot EE problem. This paper follows the trend of modeling event-type semantics but moves one step further. We argue that using the static embedding of the event type name might not be enough because a single word could be ambiguous, and we need a sentence to define the type semantics accurately. To model the definition semantics, we use two separate transformer models to project the contextualized event mentions and corresponding definitions into the same embedding space and then minimize their embedding distance via contrastive learning. On top of that, we also propose a warming phase to help the model learn the minor difference between similar definitions. We name our approach Zero-shot Event extraction with Definition (ZED). Experiments on the MAVEN dataset show that our model significantly outperforms all previous zero-shot EE methods with fast inference speed due to the disjoint design. Further experiments also show that \ModelName~can be easily applied to the few-shot setting when the annotation is available and consistently outperforms baseline supervised methods.

Efficient Zero-shot Event Extraction with Context-Definition Alignment

Reasoning is central to human intelligence. However, fallacious arguments are common, and some exacerbate problems such as spreading misinformation about climate change. In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text, together with an additional challenge set for detecting logical fallacies in climate change claims (LogicClimate). Detecting logical fallacies is a hard problem as the model must understand the underlying logical structure of the argument. We find that existing pretrained large language models perform poorly on this task. In contrast, we show that a simple structure-aware classifier outperforms the best language model by 5.46% F1 scores on Logic and 4.51% on LogicClimate. We encourage future work to explore this task since (a) it can serve as a new reasoning challenge for language models, and (b) it can have potential applications in tackling the spread of misinformation. Our dataset and code are available at https://
github.com/causalNLP/logical-fallacy

Logical Fallacy Detection

The recent literature in text classification is biased towards short text sequences (e.g., sentences or paragraphs). 
In real-world applications, multi-page multi-paragraph documents are common and they cannot be efficiently encoded by vanilla Transformer-based models. 
We compare different Transformer-based Long Document Classification (TrLDC) approaches that aim to mitigate the computational overhead of vanilla transformers to encode much longer text, namely sparse attention and hierarchical encoding methods.
We examine several aspects of sparse attention (e.g., size of local attention window, use of global attention) and hierarchical (e.g., document splitting strategy) transformers on four document classification datasets covering different domains. 
We observe a clear benefit from being able to process longer text, and, based on our results, we derive practical advice of applying Transformer-based models on long document classification tasks.

Revisiting Transformer-based Models for Long Document Classification

In this paper, we study the effects of incorporating timestamps, such as document creation dates, into generation systems. Two types of time-aware prompts are investigated: (1) textual prompts that encode document timestamps in natural language sentences; and (2) linear prompts that convert timestamps into continuous vectors. To explore extrapolation to future data points, we further introduce a new data-to-text generation dataset, TempWikiBio, containing more than 4 millions of chronologically ordered revisions of biographical articles from English Wikipedia, each paired with structured personal profiles.
Through data-to-text generation on TempWikiBio, text-to-text generation on the content transfer dataset, and summarization on XSum,
we show that linear prompts on encoder and textual prompts improve the generation quality on all datasets.
Despite having less performance drop when testing on data drawn from a later time, linear prompts focus more on non-temporal information and are less sensitive to the given timestamps, according to human evaluations and sensitivity analyses.
Meanwhile, textual prompts establish the association between the given timestamps and the output dates, yielding more factual temporal information in the output.

Time-aware Prompting for Text Generation

Large transformer models can highly improve Answer Sentence Selection (AS2) tasks, but their high computational costs prevent their use in many real-world applications. In this paper, we explore the following research question: How can we make the AS2 models more accurate without significantly increasing their model complexity? To address the question, we propose a Multiple Heads Student architecture (named CERBERUS), an efficient neural network designed to distill an ensemble of large transformers into a single smaller model. CERBERUS consists of two components: a stack of transformer layers that is used to encode inputs, and a set of ranking heads; unlike traditional distillation technique, each of them is trained by distilling a different large transformer architecture in a way that preserves the diversity of the ensemble members. The resulting model captures the knowledge of heterogeneous transformer models by using just a few extra parameters. We show the effectiveness of CERBERUS on three English datasets for AS2; our proposed approach outperforms all single-model distillations we consider, rivaling the state-of-the-art large AS2 models that have 2.7× more parameters and run 2.5× slower. Code for our model is available at https://github.com/amazon-research/wqa-cerberus.

Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems

Neural machine translation (MT) systems have been shown to perform poorly on low-resource language pairs, for which large-scale parallel data is unavailable. Making the data annotation process faster and cheaper is therefore important to ensure equitable access to MT systems. To make optimal use of a limited annotation budget, we present CHIA (choosing instances to annotate), a method for selecting instances to annotate for machine translation. Using an existing multi-way parallel dataset of high-resource languages, we first identify instances, based on model training dynamics, that are most informative for training MT models for high-resource languages. 
We find that there are cross-lingual commonalities in instances that are useful for MT model training, which we use to identify instances that will be useful to train models on a new target language. Evaluating on 20 languages from two corpora, we show that training on instances selected using our method provides an average performance improvement of 1.59 BLEU over training on randomly selected instances of the same size.

CHIA: CHoosing Instances to Annotate for Machine Translation

Machine Translation task has made great progress with the help of auto-regressive decoding paradigm and Transformer architecture. In this paradigm, though the encoder can obtain global source representations, the decoder can only use translation history to determine the current word. Previous promising works attempted to address this issue by applying a draft or a fixed-length semantic embedding as target-side global information. However, these methods either degrade model efficiency or show limitations in expressing semantics. Motivated by Functional Equivalence Theory, we extract several semantic kernels from a source sentence, each of which can express one semantic segment of the original sentence. Together, these semantic kernels can capture global semantic information, and we project them into target embedding space to guide target sentence generation. We further force our model to use semantic kernels at each decoding step through an adaptive mask algorithm. Empirical studies on various machine translation benchmarks show that our approach gains approximately an improvement of 1 BLEU score on most benchmarks over the Transformer baseline and about 1.7 times faster than previous works on average at inference time.

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