United Arab Emirates

Even though fine-tuned neural language models have been pivotal in enabling &quot;deep&quot; automatic text analysis, optimizing text representations for specific applications remains a crucial bottleneck.  In this study, we look at this problem in the context of a task from computational social science, namely modeling pairwise similarities between political parties. Our research question is what level of structural information is necessary to create robust text representation, contrasting a strongly informed approach (which uses both claim span and claim category annotations) with approaches that forgo one or both types of annotation with document structure-based heuristics. Evaluating our models on the manifestos of German parties for the 2021 federal election. We find that heuristics that maximize within-party over between-party similarity along with a normalization step lead to reliable party similarity prediction, without the need for manual annotation.

EMNLP 2022

Optimizing text representations to capture (dis)similarity between political parties

political parties

similarity

sentence representation

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
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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.

Even though fine-tuned neural language models have been pivotal in enabling "deep" automatic text analysis, optimizing text representations for specific applications remains a crucial bottleneck.  In this study, we look at this problem in the context of a task from computational social science, namely modeling pairwise similarities between political parties. Our research question is what level of structural information is necessary to create robust text representation, contrasting a strongly informed approach (which uses both claim span and claim category annotations) with approaches that forgo one or both types of annotation with document structure-based heuristics. Evaluating our models on the manifestos of German parties for the 2021 federal election. We find that heuristics that maximize within-party over between-party similarity along with a normalization step lead to reliable party similarity prediction, without the need for manual annotation.

workshop paper

## Welcome to EMNLP 2022!
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***EMNLP 2022 General Chair***
 
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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.

We propose an ACT-R cue-based retrieval model of the real-time gender predictions displayed by second language (L2) learners. The model extends a previous model of native (L1) speakers according to two central accounts in L2 sentence processing: (i) the Interference Hypothesis, which proposes that retrieval interference is higher in L2 than L1 speakers; (ii) the Lexical Bottleneck Hypothesis, which proposes that problems with gender agreement are due to weak gender representations. We tested the predictions of these accounts using data from two visual world experiments, which found that the gender predictions elicited by German possessive pronouns were delayed and smaller in size in L2 than L1 speakers. The experiments  also found a "match effect", such that when the antecedent and possessee of the pronoun had the same gender, predictions were earlier than when the two genders differed. This match effect was smaller in L2 than L1 speakers. The model implementing the Lexical Bottleneck Hypothesis captured the effects of smaller predictions, smaller match effect and delayed predictions in one of the two conditions. By contrast, the model implementing the Interference Hypothesis captured the smaller prediction effect but it showed an earlier prediction effect and an increased match effect in L2 than L1 speakers. These results provide evidence for the Lexical Bottleneck Hypothesis, and they demonstrate a method for extending computational models of L1 to L2 processing.

Computational cognitive modeling of predictive sentence processing in a second language

Targeted studies testing knowledge of subject-verb agreement (SVA) indicate that pre-trained language models encode syntactic information. We assert that if models robustly encode subject-verb agreement, they should be able to identify when agreement is correct and when it is incorrect. To that end, we propose grammatical error detection as a diagnostic probe to evaluate token-level contextual representations for their knowledge of SVA. We evaluate contextual representations at each layer from five pre-trained English language models: BERT, XLNet, GPT-2, RoBERTa and ELECTRA. We leverage public annotated training data from both English second language learners and Wikipedia edits, and report results on manually crafted stimuli for subject-verb agreement. We find that masked language models linearly encode information relevant to the detection of SVA errors, while the autoregressive models perform on par with our baseline. However, we also observe a divergence in performance when probes are trained on different training sets, and when they are evaluated on different syntactic constructions, suggesting the information pertaining to SVA error detection is not robustly encoded.

Probing for targeted syntactic knowledge through grammatical error detection

Text segmentation is a natural language processing task with popular applications, such as topic segmentation, element discourse extraction, and sentence tokenization. Much work has been done to develop accurate segmentation similarity metrics, but even the most advanced metrics used today, B, and WindowDiff, exhibit incorrect behavior due to their evaluation of boundaries in isolation. In this paper, we present a new segment-alignment based approach to segmentation similarity scoring and a new similarity metric A. We show that A does not exhibit the erratic behavior of $ and WindowDiff, quantify the likelihood of B and WindowDiff misbehaving through simulation, and discuss the versatility of alignment-based approaches for segmentation similarity scoring. We make our implementation of A publicly available and encourage the community to explore more sophisticated approaches to text segmentation similarity scoring.

An Alignment-based Approach to Text Segmentation Similarity Scoring

Notwithstanding recent advances, syntactic generalization remains a challenge for text decoders.     While some studies showed gains from incorporating source-side symbolic syntactic and semantic structure into text generation Transformers, very little work addressed the decoding of such structure.      We propose a general approach for tree decoding using a transition-based approach.     Examining the challenging test case of incorporating Universal Dependencies syntax into machine translation, we present substantial improvements on test sets that focus on syntactic generalization, while presenting improved or comparable performance     on standard MT benchmarks. Further qualitative analysis addresses cases where syntactic generalization in the vanilla Transformer decoder is inadequate and demonstrates the advantages afforded by integrating syntactic information.

Enhancing the Transformer Decoder with Transition-based Syntax

When a language model is trained to predict natural language sequences, its prediction at each moment depends on a representation of prior context. What kind of information about the prior context can language models retrieve? We tested whether language models could retrieve the exact words that occurred previously in a text. In our paradigm, language models (transformers and an LSTM) processed English text in which a list of nouns occurred twice. We operationalized retrieval as the reduction in surprisal from the first to the second list. We found that the transformers retrieved both the identity and ordering of nouns from the first list. Further, the transformers' retrieval was markedly enhanced when they were trained on a larger corpus and with greater model depth. Lastly, their ability to index prior tokens was dependent on learned attention patterns. In contrast, the LSTM exhibited less precise retrieval, which was limited to list-initial tokens and to short intervening texts. The LSTM's retrieval was not sensitive to the order of nouns and it improved when the list was semantically coherent. We conclude that transformers implemented something akin to a working memory system that could flexibly retrieve individual token representations across arbitrary delays; conversely, the LSTM maintained a coarser and more rapidly-decaying semantic gist of prior tokens, weighted toward the earliest items.

Characterizing Verbatim Short-Term Memory in Neural Language Models

In this paper, we present our system for the EvoNLP 2022 shared task Temporal Meaning Shift (TempoWiC). Different from the typically used discriminative model, we propose a generative approach based on pre-trained generation models. The basic architecture of our system is a seq2seq model where the input sequence consists of two documents followed by a question asking whether the meaning of target word changed or not, the target output sequence is a declarative sentence describing the meaning of target word changed or not. The experimental results on TempoWiC test set show that our best system (with time information) obtained an accuracy and Marco F-1 score of 68.09% and 62.59% respectively, which ranked 12th among all submitted systems. The results have shown the plausibility of using generation model for WiC tasks, meanwhile also indicate there's still room for further improvement.

MLLabs-LIG at TempoWiC 2022: A Generative Approach for Examining Temporal Meaning Shift

This paper mainly describes the dma submission to the TempoWiC task, which achieves a macro-F1 score of 77.05% and attains the first place in this task. We first explore the impact of different pre-trained language models. Then we adopt data cleaning, data augmentation, and adversarial training strategies to enhance the model generalization and robustness. For further improvement, we integrate POS information and word semantic representation using a Mixture-of-Experts (MoE) approach. The experimental results show that MoE can overcome the feature overuse issue and combine the context, POS, and word semantic features well. Additionally, we use a model ensemble method for the final prediction, which has been proven effective by many research works.

Using Deep Mixture-of-Experts to Detect Word Meaning Shift for TempoWiC

Research on multi-class text classification of short texts mainly focuses on supervised (transfer) learning approaches, requiring a finite set of pre-defined classes which is constant over time. 
This work explores deep constrained clustering (CC) as an alternative to supervised learning approaches in a setting with a dynamically changing number of classes, a task we introduce as dynamic topic discovery (DTD).
We do so by using pairwise similarity constraints instead of instance-level class labels which allow for a flexible number of classes while exhibiting a competitive performance compared to supervised approaches. 
First, we substantiate this through a series of experiments and show that CC algorithms exhibit a predictive performance similar to state-of-the-art supervised learning algorithms while requiring less annotation effort.
Second, we demonstrate the overclustering capabilities of deep CC for detecting topics in short text data sets in the absence of the ground truth class cardinality during model training.
Third, we showcase that these capabilities can be leveraged for the DTD setting as a step towards dynamic learning over time and finally, we release our codebase to nurture further research in this area.

CC-Top: Constrained Clustering for Dynamic Topic Discovery

This paper describes our methods for temporal meaning shift detection, implemented during the TempoWiC shared task. We present two systems: with and without time span data usage. Our approaches are based on the language models fine-tuned for Twitter domain. Both systems outperformed all the competition’s baselines except TimeLMs-SIM. Our best submission achieved the macro-F1 score of  70.09% and took the 7th place. This result was achieved by using diachronic language models from the TimeLMs project.

HSE at TempoWiC: Detecting Meaning Shift in Social Media with Diachronic Language Models

We present a study on the integration of time-sensitive information in lexicon-based offensive language detection systems. Our focus is on Offenseval sub-task A, aimed at detecting offensive tweets. We apply a semantic change detection algorithm over a short time span of two years to detect words whose semantics has changed and we focus particularly on those words that acquired or lost an offensive meaning between 2019 and 2020. Using the output of this semantic change detection approach, we train an SVM classifier on the Offenseval 2019 training set. We build on the already competitive SINAI system submitted to Offenseval 2019 by adding new lexical features, including those that capture the change in usage of words and their association with emerging offensive usages. We discuss the challenges, opportunities and limitations of integrating semantic change detection in offensive language detection models. Our work draws attention to an often neglected aspect of offensive language, namely that the meanings of words are constantly evolving and that NLP systems that account for this change can achieve good performance even when not trained on the most recent training data. 

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Computational cognitive modeling of predictive sentence processing in a second language

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Additive manifesto decomposition: A policy domain aware method for understanding party positioning