United States

We present an extension of the Morfessor Baseline model of unsupervised morphological segmentation (Creutz and Lagus, 2007) that incorporates abstract templates for reduplication, a typologically common but computationally underaddressed process. Through a detailed investigation that applies the model to Māori, the Indigenous language of Aotearoa New Zealand, we show that incorporating templates improves Morfessor’s ability to identify instances of reduplication, and does so most when there are multiple minimally-overlapping templates. We present an error analysis that reveals important factors to consider when applying the extended model and suggests useful future directions.

NAACL 2022

Unsupervised Morphological Segmentation in a Language with Reduplication

morfessor

māori

reduplication

morphological segmentation

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

workshop paper

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Word embeddings are growing to be a crucial resource in the field of NLP for any language. This work introduces a novel technique for static subword embeddings transfer for Indic languages from a relatively higher resource language to a genealogically related low resource language. We primarily work with Hindi-Marathi, simulating a low-resource scenario for Marathi, and confirm observed trends on Nepali. We demonstrate the consistent benefits of unsupervised morphemic segmentation on both source and target sides over the treatment performed by fastText. Our best-performing approach uses an EM-style approach to learning bilingual subword embeddings; we also show, for the first time, that a trivial ``copy-and-paste'' embeddings transfer based on even perfect bilingual lexicons is inadequate in capturing language-specific relationships. We find that our approach substantially outperforms the fastText baselines for both Marathi and Nepali on the Word Similarity task as well as WordNet-Based Synonymy Tests; on the former task, its performance for Marathi is close to that of pretrained fastText embeddings that use three orders of magnitude more Marathi data.

Subword-based Cross-lingual Transfer of Embeddings from Hindi to Marathi and Nepali

Arabic is a morphologically rich and complex language, with numerous dialectal variants. Previous efforts on Arabic morphology modeling focused on specific variants and specific domains using a range of techniques with different degrees of linguistic modeling transparency. 
In this paper we propose a new approach to modeling Arabic morphology with an eye towards multi-dialectness, resource openness, and easy extensibility and use.
We demonstrate our approach by modeling verbs from Standard Arabic and Egyptian Arabic, within a common framework, and with high coverage.

Morphotactic Modeling in an Open-source Multi-dialectal Arabic Morphological Analyzer and Generator

OSU’s inflection system is a transformer whose
input is augmented with an analogical exemplar
showing how to inflect a different word into the
target cell. In addition, alignment-based heuris-
tic features indicate how well the exemplar is
likely to match the output. OSU’s scores sub-
stantially improve over the baseline transformer
for instances where an exemplar is available,
though not quite matching the challenge win-
ner. In Part 2, the system shows a tendency to
over-apply the majority pattern in English, but
not Arabic.

OSU At at SIGMORPHON ‘22: Analogical Inflection with Rule Features

Motivations for methods in explainable artificial intelligence (XAI) often include detecting, quantifying and mitigating bias, and contributing to making machine learning models fairer. However, exactly how an XAI method can help in combating biases is often left unspecified. In this paper, we briefly review trends in explainability and fairness in NLP research, identify the current practices in which explainability methods are applied to detect and mitigate bias, and investigate the barriers preventing XAI methods from being used more widely in tackling fairness issues.

Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models

Recent large-scale natural language processing (NLP) systems use a pre-trained Large Language Model (LLM) on massive and diverse corpora as a headstart. In practice, the pre-trained model is adapted to a wide array of tasks via fine-tuning on task-specific datasets. LLMs, while effective, have been shown to memorize instances of training data thereby potentially revealing private information processed during pre-training. The potential leakage might further propagate to the downstream tasks for which LLMs are fine-tuned. On the other hand, privacy-preserving algorithms usually involve retraining from scratch, which is prohibitively expensive for LLMs. In this work, we propose a simple, easy to interpret, and computationally lightweight perturbation mechanism to be applied to an already trained model at the decoding stage. Our perturbation mechanism is model-agnostic and can be used in conjunction with any LLM. We provide theoretical analysis showing that the proposed mechanism is differentially private, and experimental results showing a privacy-utility trade-off.

Differentially Private Decoding in Large Language Models

Encoder-only transformer models have been successfully applied to different table understanding tasks, as in TAPAS. A major limitation of these architectures is that they are constrained to classification-like tasks such as cell selection or entailment detection. We present TABT5, an encoder-decoder model that generates natural language text based on tables and textual inputs. TABT5 overcomes the encoder-only limitation by incorporating a decoder component and leverages the input structure with table specific embeddings and pre-training. TABT5 achieves new state-of-the-art results on several domains, including spreadsheet formula prediction with a 15% increase in sequence accuracy, QA with a 2.5% increase in sequence accuracy and data-to-text generation with a 2.5% increase in BLEU.

Table-To-Text generation and pre-training with TabT5

Neuro-symbolic reasoning has recently seen a revival as a promising way to bridge the gap between deep learning models that manipulate continuous spaces and the symbolic representations. Despite its promise, the term ``Neuro-symbolic'' remains nebulous and is used in various contexts, from systems that rely on having a rule-based sub-component to systems trained end-to-end using techniques like reinforcement learning. In this work, we conduct a survey of its various interpretations amongst researchers in the natural language processing (NLP) community along with a survey of prior work that focuses on Neuro-symbolic methods. We posit that Neuro-symbolic models are a host of techniques that allow dense continuous representations to be informed by more abstract discrete representations. Our work serves as a bird's eye view of this promising topic that opens avenues for future work.

Characterizing Neuro-symbolic Reasoning in NLP

Within the emerging research efforts to combine structured and unstructured knowledge, many approaches incorporate factual knowledge, e.g., available in form of structured knowledge graphs (KGs), into pre-trained language models (PLMs) and then apply the knowledge-enhanced PLMs to downstream NLP tasks. However, (1) they typically only consider \textit{static} factual knowledge, whereas, e.g., knowledge graphs (KGs) also contain \textit{temporal facts} or \textit{events} indicating evolutionary relationships among entities at different timestamps. (2) PLMs cannot be directly applied to many KG tasks, such as temporal KG completion. In this paper, we focus on \textbf{e}nhancing temporal knowledge embeddings with \textbf{co}ntextualized \textbf{la}nguage representations (ECOLA). We align structured knowledge, contained in temporal knowledge graphs, with their textual descriptions extracted from news articles, and propose a novel knowledge-text prediction task to inject the abundant information from descriptions into temporal knowledge embeddings. ECOLA jointly optimizes the knowledge-text prediction objective and the temporal knowledge embeddings, which can simultaneously take full advantage of textual and knowledge information. The proposed fusion method is model-agnostic and can be combined with potentially any temporal KG model. For training ECOLA, we introduce three temporal KG datasets with aligned textual descriptions. Experimental results on the temporal knowledge graph completion task show that ECOLA outperforms state-of-the-art temporal KG models by a large margin. The proposed datasets can serve as new temporal KG benchmarks and facilitate future research on structured and unstructured knowledge integration.

Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations

To tailor a learning system to the student's level and needs, we must consider the characteristics of the learning content, such as its difficulty. While natural language processing allows us to represent text efficiently, the meaningful representation of mathematical formulas in an educational context is still understudied. This paper adopts structural embeddings as a possible way to bridge this gap. Our experiments validate the approach using publicly available datasets to show that incorporating syntactic information can improve performance in predicting the exercise difficulty.

Structural information in mathematical formulas for exercise difficulty prediction: a comparison of NLP representations

Recent advances in natural language processing and transformer-based models have made it easier to implement accurate, automated English speech assessments. Yet, without careful examination, applications of these models may exacerbate social prejudices based on gender and race. This study addresses the need to examine potential biases of transformer-based models in the context of automated English speech assessment. For this purpose, we developed a BERT-based automated speech assessment system and investigated gender and racial bias of examinees' automated scores. Gender and racial bias was measured by examining differential item functioning (DIF) using an item response theory framework. Preliminary results, which focused on a single verbal-response item, showed no statistically significant DIF based on gender or race for automated scores.

Unsupervised Morphological Segmentation in a Language with Reduplication

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