Croatia

This paper investigates a crucial aspect of mental health by exploring the detection of suicidal ideation in spoken phone conversations between callers and counselors at a suicide prevention hotline.
These conversations can be lengthy, noisy, and cover a broad range of topics, making it challenging for NLP models to accurately identify the caller&#39;s suicidal ideation. To address these difficulties, we introduce a novel, self-adaptive approach that identifies the most critical utterances that the NLP model can more easily distinguish. 
The experiments use real-world Lifeline transcriptions, expertly labeled, and show that our approach outperforms the baseline models in overall performance with an F-score of 66.01\%. 
In detecting the most dangerous cases, our approach achieves a significantly higher F-score of 65.94\% compared to the baseline models, an improvement of 8.9\%. 
The selected utterances can also provide valuable insights for suicide prevention research. 
Furthermore, our approach demonstrates its versatility by showing its effectiveness in sentiment analysis, making it a valuable tool for NLP applications beyond the healthcare domain.

EACL 2023

Self-Adapted Utterance Selection for Suicidal Ideation Detection in Lifeline Conversations

conversation understanding

self-adapted utterance selection

suicidal ideation detection

This paper investigates a crucial aspect of mental health by exploring the detection of suicidal ideation in spoken phone conversations between callers and counselors at a suicide prevention hotline.
These conversations can be lengthy, noisy, and cover a broad range of topics, making it challenging for NLP models to accurately identify the caller's suicidal ideation. To address these difficulties, we introduce a novel, self-adaptive approach that identifies the most critical utterances that the NLP model can more easily distinguish. 
The experiments use real-world Lifeline transcriptions, expertly labeled, and show that our approach outperforms the baseline models in overall performance with an F-score of 66.01\%. 
In detecting the most dangerous cases, our approach achieves a significantly higher F-score of 65.94\% compared to the baseline models, an improvement of 8.9\%. 
The selected utterances can also provide valuable insights for suicide prevention research. 
Furthermore, our approach demonstrates its versatility by showing its effectiveness in sentiment analysis, making it a valuable tool for NLP applications beyond the healthcare domain.

poster

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

Understanding empathy in text dialogue data is a difficult, yet critical, skill for effective human-machine interaction. In this work, we ask whether systems are making meaningful progress on this challenge. We consider a simple model that checks if an input utterance is similar to a small set of empathetic examples. Crucially, the model does not look at what the utterance is a response to, i.e., the dialogue context. This model performs comparably to other work on standard benchmarks and even outperforms state-of-the-art models for empathetic rationale extraction by 16.7 points on T-F1 and 4.3 on IOU-F1. This indicates that current systems rely on the surface form of the response, rather than whether it is suitable in context. To confirm this, we create examples with dialogue contexts that change the interpretation of the response and show that current systems continue to label utterances as empathetic. We discuss the implications of our findings, including improvements for empathetic benchmarks and how our model can be an informative baseline.

Empathy Identification Systems are not Accurately Accounting for Context

Information extraction (IE) and summarization are closely related, both tasked with presenting a subset of the information contained in a natural language text. However, while IE extracts structural representations, summarization aims to abstract the most salient information into a generated text summary -- thus potentially encountering the technical limitations of current text generation methods (e.g., hallucination). To mitigate this risk, this work uses structured IE graphs to enhance the abstractive summarization task. Specifically, we focus on improving Multi-Document Summarization (MDS) performance by using cross-document IE output, incorporating two novel components: (1) the use of auxiliary entity and event recognition systems to focus the summary generation model; (2) incorporating an alignment loss between IE nodes and their text spans to reduce inconsistencies between the IE graphs and text representations. Operationally, both the IE nodes and corresponding text spans are projected into the same embedding space and pairwise distance is minimized. Experimental results on multiple MDS benchmarks show that summaries generated by our model are more factually consistent with the source documents than baseline models while maintaining the same level of abstractiveness.

Enhancing Multi-Document Summarization with Cross-Document Graph-based Information Extraction

Performance prediction for Natural Language Processing (NLP) seeks to reduce the experimental burden resulting from the myriad of different evaluation scenarios, e.g., the combination of languages used in multilingual transfer. In this work, we explore the framework of
Bayesian matrix factorisation for performance prediction, as many experimental settings in NLP can be naturally represented in matrix format. Our approach outperforms the state-of-the-art in several NLP benchmarks, including machine translation and cross-lingual entity linking. Furthermore, it also avoids hyperparameter tuning and is able to provide uncertainty estimates over predictions.

Performance Prediction via Bayesian Matrix Factorisation for Multilingual Natural Language Processing Tasks

Two types of topic modeling predominate: generative methods that employ probabilistic latent models and clustering methods that identify semantically coherent groups. This paper newly presents UTopic (Unified neural Topic model via contrastive learning and term weighting) that combines the advantages of these two types. UTopic uses contrastive learning and term weighting to learn knowledge from a pretrained language model and discover influential terms from semantically coherent clusters. Experiments show that the generated topics have a high-quality topic-word distribution in terms of topic coherence, outperforming existing baselines across multiple topic coherence measures. We demonstrate how our model can be used as an add-on to existing topic models and improve their performance.

Unified Neural Topic Model via Contrastive Learning and Term Weighting

Temporal Moment Localization is a challenging multi-modal task which aims to identify the start and end timestamps of a moment of interest in an input untrimmed video, given a query in natural language. Solving this task correctly requires understanding the temporal relationships in the entire input video, but processing such long inputs and reasoning about them is memory and computationally expensive. In light of this issue, we propose Stochastic Bucket-wise Feature Sampling (SBFS), a stochastic sampling module that allows methods to process long videos at a constant memory footprint. We further combine SBFS with a new consistency loss to propose Locformer, a Transformer-based model that can process videos as long as 18 minutes. We test our proposals on relevant benchmark datasets, showing that not only can Locformer achieve excellent results, but also that our sampling is more effective than competing counterparts. Concretely, SBFS consistently improves the performance of prior work, by up to 3.13\textbackslash{}\% in the mean temporal IoU, leading to a new state-of-the-art performance on Charades-STA and YouCookII, while also obtaining up to 12.8x speed-up at testing time and reducing memory requirements by up to 5x.

Memory-efficient Temporal Moment Localization in Long Videos

Document-level relation extraction (DocRE) is the task of identifying all relations between each entity pair in a document. Evidence, defined as sentences containing clues for the relationship between an entity pair, has been shown to help DocRE systems focus on relevant texts, thus improving relation extraction. However, evidence retrieval (ER) in DocRE faces two major issues: high memory consumption and limited availability of annotations. This work aims at addressing these issues to improve the usage of ER in DocRE. First, we propose DREEAM, a memory-efficient approach that adopts evidence information as the supervisory signal, thereby guiding the attention modules of the DocRE system to assign high weights to evidence. Second, we propose a self-training strategy for DREEAM to learn ER from automatically-generated evidence on massive data without evidence annotations. Experimental results reveal that our approach exhibits state-of-the-art performance on the DocRED benchmark for both DocRE and ER. To the best of our knowledge, DREEAM is the first approach to employ ER self-training.

DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction

Pretrained multilingual language models (LMs) can be successfully transformed into multilingual sentence encoders (SEs; e.g., LaBSE, xMPNet) via additional fine-tuning or model distillation with parallel data. However, it remains unclear how to best leverage them to represent sub-sentence lexical items (i.e., words and phrases) in cross-lingual lexical tasks. In this work, we probe SEs for the amount of cross-lingual lexical knowledge stored in their parameters, and compare them against the original multilingual LMs. We also devise a simple yet efficient method for exposing the cross-lingual lexical knowledge by means of additional fine-tuning through inexpensive contrastive learning that requires only a small amount of word translation pairs. Using bilingual lexical induction (BLI), cross-lingual lexical semantic similarity, and cross-lingual entity linking as lexical probing tasks, we report substantial gains on standard benchmarks (e.g., +10 Precision@1 points in BLI). The results indicate that the SEs such as LaBSE can be 'rewired' into effective cross-lingual lexical encoders via the contrastive learning procedure, and that it is possible to expose more cross-lingual lexical knowledge compared to using them as off-the-shelf SEs. This way, we also provide an effective tool for harnessing 'covert' multilingual lexical knowledge hidden in multilingual sentence encoders.

Probing Cross-Lingual Lexical Knowledge from Multilingual Sentence Encoders

We use insights from research on American Sign Language (ASL) phonology to train models for isolated sign language recognition (ISLR), a step towards automatic sign language understanding. Our key insight is to explicitly recognize the role of phonology in sign production to achieve more accurate ISLR than existing work which does not consider sign language phonology. We train ISLR models that take in pose estimations of a signer producing a single sign to predict not only the sign but additionally its phonological characteristics, such as the handshape. These auxiliary predictions lead to a nearly 9\% absolute gain in sign recognition accuracy on the WLASL benchmark, with consistent improvements in ISLR regardless of the underlying prediction model architecture. This work has the potential to accelerate linguistic research in the domain of signed languages and reduce communication barriers between deaf and hearing people.

Improving Sign Recognition with Phonology

In this work, we explore techniques for augmenting interactive agents with information from symbolic modules, much like humans use tools like calculators and GPS systems to assist with arithmetic and navigation. We test our agent's abilities in text games -- challenging benchmarks for evaluating the multi-step reasoning abilities of game agents in grounded, language-based environments. Our experimental study indicates that injecting the actions from these symbolic modules into the action space of a behavior cloned transformer agent increases performance on four text game benchmarks that test arithmetic, navigation, sorting, and common sense reasoning by an average of 22\%, allowing an agent to reach the highest possible performance on unseen games. This action injection technique is easily extended to new agents, environments, and symbolic modules.

Behavior Cloned Transformers are Neurosymbolic Reasoners

Deep neural models for named entity recognition (NER) have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations. However, the costs of acquiring such additional information are generally prohibitive. In this paper, we present a novel two-stage framework (AutoTriggER) to improve NER performance by automatically generating and leveraging ``entity triggers'' which are human-readable cues in the text that help guide the model to make better decisions. Our framework leverages post-hoc explanation to generate rationales and strengthens a model's prior knowledge using an embedding interpolation technique. This approach allows models to exploit triggers to infer entity boundaries and types instead of solely memorizing the entity words themselves. Through experiments on three well-studied NER datasets, AutoTriggER shows strong label-efficiency, is capable of generalizing to unseen entities, and outperforms the RoBERTa-CRF baseline by nearly 0.5 F1 points on average.

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Self-Adapted Utterance Selection for Suicidal Ideation Detection in Lifeline Conversations

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Empathy Identification Systems are not Accurately Accounting for Context