United States

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.

NAACL 2022

Differentially Private Decoding in Large Language Models

private prediction

langauge modeling

differential privacy

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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Please Note: The EMNLP registration system is not currently connected to the underline site as we are still in the process of building out EMNLP 2021. You will receive access instructions from underline the week of November 1st. 

Access is given only to EMNLP upon registration, if you have not registered please do so [here](https://2021.emnlp.org/registration).

Registered attendees will receive access the week of November 1st.

EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

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

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.

Using Item Response Theory to Measure Gender and Racial Bias of a BERT-based Automated English Speech Assessment System

Transactivity is a valued collaborative process, which has been associated with elevated learning gains, collaborative product quality, and knowledge transfer within teams. Dynamic forms of collaboration support have made use of real time monitoring of transactivity, and automation of its analysis has been affirmed as valuable to the field. Early models were able to achieve high reliability within restricted domains. More recent approaches have achieved a level of generality across learning domains. In this study, we investigate generalizability of models developed primarily in computer science courses to a new student population, namely, masters students in a leadership course, where we observe strikingly different patterns of transactive exchange than in prior studies. This difference prompted both a reformulation of the coding standards and innovation in the modeling approach, both of which we report on here.

Taking Transactivity to the Next Level

Mental distress like depression and anxiety contribute to the largest proportion of the global burden of diseases. Automated diagnosis systems of such disorders, empowered by recent innovations in Artificial Intelligence, can pave the way to reduce the suffering of the affected individuals. The development of such systems requires information-rich and balanced corpora. In this work, we introduce a novel mental distress analysis audio dataset DEPAC, labeled based on established thresholds on depression and anxiety standard screening tools. This large dataset comprises multiple speech tasks per individual, as well as relevant demographic information. Alongside, we present a feature set consisting of hand-curated acoustic and linguistic features, which were found effective in identifying signs of mental illnesses in human speech. Finally, we justify the quality and effectiveness of our proposed audio corpus and feature set in predicting depression severity by comparing the performance of baseline machine learning models built on this dataset with baseline models trained on other well-known depression corpora.

DEPAC: A Corpus for Depression and Anxiety Detection from Speech

The mental health risks of the COVID-19 pandemic are magnified for medical professionals, such as doctors and nurses. To track conversational markers of psychological distress and coping strategies, we analyzed 67.25 million words written by self-identified healthcare workers (N = 5,409; 60.5% nurses, 39.5% physicians) on Reddit beginning in June 2019. Dictionary-based measures revealed increasing emotionality (including more positive and negative emotion and more swearing), social withdrawal (less affiliation and empathy, more "they" pronouns), and self-distancing (fewer "I" pronouns) over time. Several effects were strongest for conversations that were least health-focused and self-relevant, suggesting that long-term changes in social and emotional behavior are general and not limited to personal or work-related experiences. Understanding protective and risky coping strategies used by healthcare workers during the pandemic is fundamental for maintaining mental health among front-line workers during periods of chronic stress, such as the COVID-19 pandemic.

Tracking Mental Health Risks and Coping Strategies in Healthcare Workers’ Online Conversations Across the COVID-19 Pandemic

In this shared task, we focus on detecting mental health signals in Reddit users' posts through two main challenges: A) capturing mood changes (anomalies) from the longitudinal set of posts (called timelines), and B) assessing the users' suicide risk- levels. Our approaches leverage emotion recognition on linguistic content by computing emotion/sentiment scores using pre-trained BERTs on users' posts and feeding them to machine learning models, including XGBoost, Bi-LSTM, and logistic regression. For Task-A, we detect longitudinal anomalies using a sequence-to- sequence (seq2seq) autoencoder and capture regions of mood deviations. For Task- B, our two models utilize the BERT emotion/sentiment scores. The rst computes emotion bandwidths and merges them with n-gram features, and employs logistic regression to detect users' suicide risk levels. The second model predicts suicide risk on the timeline level using a Bi-LSTM on Task-A results and sentiment scores.
Our results outperformed most participating teams and ranked in the top two in Task-A. In Task-B, our methods surpass all others and return the best macro and micro F1 scores.

Emotionally-Informed Models for Detecting Moments of Change and Suicide Risk Levels in Longitudinal Social Media Data

Transformer-based models have shown promising performance in numerous NLP tasks. However, recent work has shown the limitation of such models in showing compositional generalization, which requires models to generalize to novel 
compositions of known concepts. In this work, we explore two strategies for compositional generalization on the task of kinship prediction from stories: (1) data augmentation and (2) predicting and using intermediate structured representation (in form of kinship graphs). Our experiments show that data augmentation boosts generalization performance by around 20% on average relative to a baseline model from prior work not using these strategies. However, predicting and using intermediate kinship graphs leads to a deterioration in the generalization of kinship prediction by around 50% on average relative to models that only leverage data augmentation.

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

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Learning and Evaluating a Differentially Private Pre-trained Language Model