Korea, Republic of

A significant number of studies apply acoustic and linguistic characteristics of human speech as prominent markers of dementia and depression. However, studies on discriminating depression from dementia are rare. Co-morbid depression is frequent in dementia and these clinical conditions share many overlapping symptoms, but the ability to distinguish between depression and dementia is essential as depression is often curable. In this work, we investigate the ability of clustering approaches in distinguishing between depression and dementia from human speech. We introduce a novel aggregated dataset, which combines narrative speech data from multiple conditions, i.e., Alzheimer&#39;s disease, mild cognitive impairment, healthy control, and depression. We compare linear and non-linear clustering approaches and show that non-linear clustering techniques distinguish better between distinct disease clusters. Our interpretability analysis shows that the main differentiating symptoms between dementia and depression are acoustic abnormality, repetitiveness (or circularity) of speech, word finding difficulty, coherence impairment, and differences in lexical complexity and richness.

COLING 2022

Data-driven Approach to Differentiating between Depression and Dementia from Noisy Speech and Language Data

symptoms

data-driven symptoms

depression

dementia

dimensionality reduction

language

speech

clustering

[![](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.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!<br><br><br>

To gain access to this event page you are required to register.<br> Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

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

A significant number of studies apply acoustic and linguistic characteristics of human speech as prominent markers of dementia and depression. However, studies on discriminating depression from dementia are rare. Co-morbid depression is frequent in dementia and these clinical conditions share many overlapping symptoms, but the ability to distinguish between depression and dementia is essential as depression is often curable. In this work, we investigate the ability of clustering approaches in distinguishing between depression and dementia from human speech. We introduce a novel aggregated dataset, which combines narrative speech data from multiple conditions, i.e., Alzheimer's disease, mild cognitive impairment, healthy control, and depression. We compare linear and non-linear clustering approaches and show that non-linear clustering techniques distinguish better between distinct disease clusters. Our interpretability analysis shows that the main differentiating symptoms between dementia and depression are acoustic abnormality, repetitiveness (or circularity) of speech, word finding difficulty, coherence impairment, and differences in lexical complexity and richness.

workshop paper

The paper presents our system for coreference
resolution in Polish. We compare the system
with previous works for the Polish language
as well as with the multilingual approach in
the CRAC 2022 Shared Task on Multilingual
Coreference Resolution thanks to a universal,
multilingual data format and evaluation tool.
We discuss the accuracy, computational performance, and evaluation approach of the new System which is a faster, end-to-end solution.


Coreference Resolution for Polish: Improvements within the CRAC 2022 Shared Task

We describe the winning submission to the CRAC 2022 Shared Task on Multilingual Coreference Resolution. Our system first solves mention detection and then coreference linking on the retrieved spans with an antecedent-maximization approach, and both tasks are fine-tuned jointly with shared Transformer weights. We report results of fine-tuning a wide range of pretrained models. The center of this contribution are fine-tuned multilingual models. We found one large multilingual model with sufficiently large encoder to increase performance on all datasets across the board, with the benefit not limited only to the underrepresented languages or groups of typologically relative languages.

Effectivity of Multilingual Models for Coreference Resolution

Code-mixing (CM) is a frequently observed phenomenon on social media platforms in multilingual societies such as India. While the increase in code-mixed content on these platforms provides good amount of data for studying various aspects of code-mixing, the lack of automated text analysis tools makes such studies difficult. To overcome the same, tools such as language identifiers and parts of-speech (POS) taggers for analysing code-mixed data have been developed. One such tool is Named Entity Recognition (NER), an important Natural Language Processing (NLP) task, which is not only a subtask of Information Extraction, but is also needed for downstream NLP tasks such as semantic role labeling. While entity extraction from social media data is generally difficult due to its informal nature, code-mixed data further complicates the problem due to its informal, unstructured and incomplete information. 
In this work, we present the first ever corpus for Kannada-English code-mixed social media data with the corresponding named entity tags for NER. We provide strong baselines with machine learning classification models such as CRF, Bi-LSTM, and Bi-LSTM-CRF on our corpus with word, character, and lexical features.


"Kanglish alli names!" Named Entity Recognition for Kannada-English Code-Mixed Social Media Data

The tremendous increase in social media usage for sharing Television (TV) experiences has provided a unique opportunity in the Public Health and Marketing sectors to understand viewer engagement and attitudes through viewer-generated content on social media. However, this opportunity also comes with associated technical challenges. Specifically, given a televised event and related tweets about
this event, we need methods to effectively align these tweets and the corresponding event. 
In this paper, we consider the specific ecosystem of the Superbowl 2020 and map viewer tweets to advertisements they are referring to. Our proposed model, AdBERT, is an effective few-shot learning framework that can
handle the technical challenges of establishing ad-relatedness, class imbalance as well as the scarcity of labeled data. As part of this study, we have curated and developed two datasets that can prove useful for Social TV research: 1) dataset of ad-related tweets and 2) dataset of ad descriptions of Superbowl advertisements. Explaining connections to SentenceBERT, we describe the advantages of AdBERT that allow us to make the most out of a challenging and interesting dataset that we will open-source.

AdBERT: An Effective Few Shot Learning Framework for Aligning Tweets to Superbowl Advertisements

Gender analysis of Twitter can reveal important
socio-cultural differences between male and female
users. There has been a significant effort
to analyze and automatically infer gender in the
past for most widely spoken languages’ content,
however, to our knowledge very limited work
has been done for Arabic. In this paper, we
perform an extensive analysis of differences
between male and female users in the Arabic
Twitter-sphere. We study differences in user
engagement, topics of interest, and the gender
gap in professions. Along with gender analysis,
we also propose a method to infer gender
by utilizing usernames, profile pictures, tweets,
and networks of friends. In order to do so, we
manually annotated gender and locations for
∼167K Twitter accounts associated with ∼92K
user location, which we make publicly available.
1 Our proposed gender inference method
achieves an F1 score of 82.1% (47.3% higher
than the majority baseline). We also developed
a demo and made it publicly available.

https://alt.qcri.org/resources/ArabGend.zip
https://asad.qcri.org/

ArabGend: Gender Analysis and Inference on Arabic Twitter

Persona and Knowledge dual context open-domain chat is a novel dialogue generation task introduced recently. While Persona and Knowledge is each interesting context of open-domain dialogue, the combination of both has not been well studied. We tackle Persona-Knowledge identification and response generation tasks in this paper. We define Persona and Knowledge Dual Context Identification as the task to identify Persona and Knowledge jointly for a given dialogue. We develop novel multi-context retrieval method that utilizes all contexts of a dialogue simultaneously while also requiring limited training via zero-shot inference due to compatibility with neural Q & A models. Furthermore, we perform dialogue generation with various decoding techniques and illustrate crucial elements. We achieve SOTA across official metrics with 90%+ Grounding accuracy for both Persona and Knowledge Retrieval and 23.62 SacreBLEU score for Response Generation on Call For Customized Conversation benchmark.

Persona-Knowledge Dialogue Multi-Context Retrieval and Enhanced Decoding Methods

Focus on FoCus: Is FoCus focused on Context, Knowledge and Persona? 
This is an empirical study on the persona-knowledge-based dialogue system.

Focus on FoCus: Is FoCus focused on Context, Knowledge and Persona?

This is an organizers’ report of the competition on argument mining systems dealing with English tweets about COVID-19 health mandates. This competition was held within the framework of the SMM4H 2022 Workshop. During the competition, the participants were offered two subtasks: stance detection and premise classification. We present a manually annotated corpus containing 6,156 short posts from Twitter on three topics related to the COVID-19 pandemic: school closures, stay-at-home orders, and wearing masks. We hope the prepared dataset will support further research on argument mining in the health field

SMM4H Task 2: Stance and premise detection in tweets about COVID-19 health mandates

Researchers across disciplines use Twitter geolocation tools to filter data for desired locations. These tools have largely been trained and tested on English tweets, often originating in the United States from almost a decade ago. Despite the importance of these tools for data curation, the impact of tweet language, country of origin, and creation date on tool performance remains largely unknown. We explore these issues with Carmen, a popular tool for Twitter geolocation. To support this study we introduce Carmen 2.0, a major update which includes the incorporation of GeoNames, a gazetteer that provides much broader coverage of locations. We evaluate using two new Twitter datasets, one for multilingual, multiyear geolocation evaluation, and another for usage trends over time. We found that language, country origin, and time does impact geolocation tool performance.

Changes in Tweet Geolocation over Time: A Study with Carmen 2.0

Entity Linking (EL) is the gateway into Knowledge Bases. Recent advances in EL utilize dense retrieval approaches for Candidate Generation, which addresses some of the shortcomings of the Lookup based approach of matching NER mentions against pre-computed dictionaries. In this work, we show that in the domain of Tweets, such methods suffer as users often include informal spelling, limited context, and lack of specificity, among other issues. We investigate these challenges on a large and recent Tweets benchmark for EL, empirically evaluate lookup and dense retrieval approaches, and demonstrate a hybrid solution using long contextual representation from Wikipedia is necessary to achieve considerable gains over previous work, achieving 0.93 recall.

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Data-driven Approach to Differentiating between Depression and Dementia from Noisy Speech and Language Data

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Coreference Resolution for Polish: Improvements within the CRAC 2022 Shared Task

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