United Arab Emirates

Are the predictions of humans and language models affected by similar things? Research suggests that while comprehending language, humans make predictions about upcoming words, with more predictable words being processed more easily. However, evidence also shows that humans display a similar processing advantage for highly anomalous words when these words are semantically related to the preceding context or to the most probable continuation. Using stimuli from 3 psycholinguistic experiments, we find that this is also almost always also the case for 8 contemporary transformer language models (BERT, ALBERT, RoBERTa, XLM-R, GPT-2, GPT-Neo, GPT-J, and XGLM). We then discuss the implications of this phenomenon for our understanding of both human language comprehension and the predictions made by language models.

EMNLP 2022

Collateral facilitation in humans and language models

event-related brain potentials

n400

human language comprehension

transformer language models

natural language processing

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

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Welcome!
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.

This paper investigates how hate speech varies in systematic ways according to the identities it targets. Across multiple hate speech datasets annotated for targeted identities, we find that classifiers trained on hate speech targeting specific identity groups struggle to generalize to other targeted identities. This provides empirical evidence for differences in hate speech by target identity; we then investigate which patterns structure this variation. We find that the targeted demographic category (e.g. gender/sexuality or race/ethnicity) appears to have a greater effect on the language of hate speech than does the relative social power of the targeted identity group. We also find that words associated with hate speech targeting specific identities often relate to stereotypes, histories of oppression, current social movements, and other social contexts specific to identities. These experiments suggest the importance of considering targeted identity, as well as the social contexts associated with these identities, in automated hate speech classification

How Hate Speech Varies by Target Identity: A Computational Analysis

Conventional natural language process (NLP) generation models are trained offline with a given dataset for a particular task, which is referred to as isolated learning. Research on sequence-to-sequence language generation aims to study continual learning model to constantly learning from sequentially encountered tasks. However, continual learning studies often suffer from catastrophic forgetting, a persistent challenge for lifelong learning. In this paper, we present a novel NLP transformer model that attempts to mitigate catastrophic forgetting in online continual learning from a new perspective, i.e., attention calibration. We model the attention in the transformer as a calibrated unit in a general formulation, where the attention calibration could give benefits to balance the stability and plasticity of continual learning algorithms through influencing both their forward inference path and backward optimization path. Our empirical experiments, paraphrase generation and dialog response generation, demonstrate that this work outperforms state-of-the-art models by a considerable margin and effectively mitigate the forgetting.

Continual Learning for Natural Language Generations with Transformer Calibration

Social media plays an increasing role in our communication with friends and family, and in our consumption of entertainment and information. Hence, to design effective ranking functions for posts on social media, it would be useful to predict the affective responses of a post (e.g., whether it is likely to elicit feelings of entertainment, inspiration, or anger). Similar to work on emotion detection (which focuses on the affect of the publisher of the post), the traditional approach to recognizing affective response would involve an expensive investment in human annotation of training data.   We create and publicly release CARE DB, a dataset of 230k social media post annotations according to seven affective responses using the Common Affective Response Expression (CARE) method. The CARE method is a means of leveraging the signal that is present in comments that are posted in response to a post, providing high-precision evidence about the affective response to the post without human annotation. Unlike human annotation, the annotation process we describe here can be iterated upon to expand the coverage of the method, particularly for new affective responses. We present experiments that demonstrate that the CARE annotations compare favorably with crowdsourced annotations. Finally, we use CARE DB to train competitive BERT-based models for predicting affective response as well as emotion detection, demonstrating the utility of the dataset for related tasks.

That's so cute!: The CARE Dataset for Affective Response Detection

While there is increasing concern about the interpretability of neural models, the evaluation of interpretability remains an open problem, due to the lack of proper evaluation datasets and metrics. In this paper, we present a novel benchmark to evaluate the interpretability of both neural models and saliency methods. This benchmark covers three representative NLP tasks: sentiment analysis, textual similarity and reading comprehension, each provided with both English and Chinese annotated data. In order to precisely evaluate the interpretability, we provide token-level rationales that are carefully annotated to be sufficient, compact and comprehensive. We also design a new metric, i.e., the consistency between the rationales before and after perturbations, to uniformly evaluate the interpretability on different types of tasks. Based on this benchmark, we conduct experiments on three typical models with three saliency methods, and unveil their strengths and weakness in terms of interpretability. We will release this benchmark (https://www.luge.ai/#/luge/task/taskDetail?taskId=15) and hope it can facilitate the research in building trustworthy systems.

A Fine-grained Interpretability Evaluation Benchmark for Neural NLP

A number of papers have recently argued in favor of using artificially generated languages to investigate the inductive biases of linguistic models, or to develop models for low-resource languages with underrepresented typologies. But the promise of artificial languages comes with a caveat: if these artificial languages are not sufficiently reflective of natural language, then using them as a proxy may lead to inaccurate conclusions. In this paper, we take a step towards increasing the realism of artificial language by introducing a variant of indexed grammars that draw their weights from hierarchical Pitman-Yor processes. We show that this framework generates languages that emulate the statistics of natural language corpora better than the current approach of directly formulating weighted context-free grammars.

Towards More Natural Artificial Languages

Structural probing work has found evidence for latent syntactic information in pre-trained language models. However, much of this analysis has focused on monolingual models, and analyses of multilingual models have employed correlational methods that are confounded by the choice of probing tasks. In this study, we causally probe multilingual language models (XGLM and multilingual BERT) as well as monolingual BERT-based models across various languages; we do this by performing counterfactual perturbations on neuron activations and observing the effect on models' subject-verb agreement probabilities. We observe where in the model and to what extent syntactic agreement is encoded in each language. We find significant neuron overlap across languages in autoregressive multilingual language models, but not masked language models. We also find two distinct layer-wise effect patterns and two distinct sets of neurons used for syntactic agreement, depending on whether the subject and verb are separated by other tokens. Finally, we find that behavioral analyses of language models are likely underestimating how sensitive masked language models are to syntactic information.

Causal Analysis of Syntactic Agreement Neurons in Multilingual Language Models

With the rise of online hate speech, automatic detection of Hate Speech, Offensive texts as a natural language processing task is getting popular. However, very little research has been done to detect unintended social bias from these toxic language datasets. This paper introduces a new dataset ToxicBias curated from the existing dataset of Kaggle competition named "Jigsaw Unintended Bias in Toxicity Classification". We aim to detect social biases, their categories, and targeted groups. The dataset contains instances annotated for five different bias categories, viz., gender, race/ethnicity, religion, political, and LGBTQ. We train transformer-based models using our curated datasets and report baseline performance for bias identification, target generation, and bias implications. Model biases and their mitigation are also discussed in detail. Our study motivates a systematic extraction of social bias data from toxic language datasets.

Detecting Unintended Social Bias in Toxic Language Datasets

Despite neural language models qualitatively capturing many human linguistic behaviors, recent work has demonstrated that they underestimate the true processing costs of ungrammatical structures.  We extend these more fine-grained comparisons between humans and models by investigating the interaction between Principle B and coreference processing.  While humans use Principle B to block certain structural positions from affecting their incremental processing, we find that GPT-based language models are influenced by ungrammatical positions. We conclude by relating the mismatch between neural models and humans to properties of training data and suggest that certain aspects of human processing behavior do not directly follow from linguistic data.

Incremental Processing of Principle B: Mismatches Between Neural Models and Humans

We introduce a constraint-based parser for Minimalist Grammars (MG), implemented as a working computer program, that falls within the long established "Parsing as Deduction" framework. The parser takes as input an MG lexicon and a (partially specified) pairing of sound with meaning - i.e. a word sequence paired with a semantic representation - and, using an axiomatized logic, declaratively deduces syntactic derivations (i.e. parse trees) that comport with the specified interface conditions. The parser is built on the first axiomatization of MGs to use Satisfiability Modulo Theories (SMT), encoding in a constraint-based way the principles of minimalist syntax. The parser operates via a novel solution method: it assembles an SMT model of an MG derivation, translates the inputs into SMT formulae that constrain the model, and then solves the model using the Z3 SMT-solver, a high-performance automatic theorem prover; as the SMT-model has finite size (being bounded by the inputs), it is decidable and thus solvable in finite time. The output derivation is then recovered from the model solution. To demonstrate this, we run the parser on several representative inputs and examine how the output derivations differ when the inputs are partially vs. fully specified. We conclude by discussing the parser's extensibility and how a linguist can use it to automatically identify: (i) dependencies between input interface conditions and principles of syntax, and (ii) contradictions or redundancies between the model axioms encoding principles of syntax.

Parsing as Deduction Revisited: Using an Automatic Theorem Prover to Solve an SMT Model of a Minimalist Parser

Language models are often trained on text alone, without additional grounding. There is debate as to how much of natural language semantics can be inferred from such a procedure. We prove that entailment judgments between sentences can be extracted from an ideal language model that has perfectly learned its target distribution, assuming the training sentences are generated by Gricean agents, i.e., agents who follow fundamental principles of communication from the linguistic theory of pragmatics. We also show entailment judgments can be decoded from the predictions of a language model trained on such Gricean data. Our results reveal a pathway for understanding the semantic information encoded in unlabeled linguistic data and a potential framework for extracting semantics from language models.

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How Hate Speech Varies by Target Identity: A Computational Analysis

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