Korea, Republic of

Medical Relation Extraction (MRE) task aims to extract relations between entities in medical texts. Traditional relation extraction methods achieve impressive success by exploring the syntactic information, e.g., dependency tree. However, the quality of the 1-best dependency tree for medical texts produced by an out-of-domain parser is relatively limited so that the performance of medical relation extraction method may degenerate. To this end, we propose a method to jointly model semantic and
syntactic information from medical texts based on causal explanation theory. We generate dependency forests consisting of the semanticembedded 1-best dependency tree. Then, a taskspecific causal explainer is adopted to prune the dependency forests, which are further fed into a designed graph convolutional network to learn the corresponding representation for downstream task. Empirically, the various comparisons on benchmark medical datasets demonstrate the effectiveness of our model.

COLING 2022

Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest

medical relation extraction、dependency forest、causal pruning

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.

technical paper

[![](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.

One of AI’s grand challenges consists in the development of autonomous agents with communication systems offering the robustness, flexibility and adaptivity found in human languages. While the processes through which children acquire language are by now relatively well understood, a faithful computational operationalisation of the underlying mechanisms is still lacking. Two main cognitive processes are involved in child language acquisition. First, children need to reconstruct the intended meaning of observed utterances, a process called intention reading. Then, they can gradually abstract away from concrete utterances in a process called pattern finding and acquire productive schemata that generalise over form and meaning. In this paper, we introduce a mechanistic model of the intention reading process and its integration with pattern finding capacities. Concretely, we present an agent-based simulation in which an agent learns a grammar that enables them to ask and answer questions about a scene. This involves the reconstruction of queries that correspond to observed questions based on the answer and scene alone, and the generalization of linguistic schemata based on these reconstructed question-query pairs. The result is a productive grammar which can be used to map between natural language questions and queries without ever having observed the queries.

Language Acquisition through Intention Reading and Pattern Finding

Machine-learning-as-a-service (MLaaS) has attracted millions of users to their splendid large-scale models. Although published as black-box APIs, the valuable models behind these services are still vulnerable to imitation attacks. Recently, a series of works have demonstrated that attackers manage to steal or extract the victim models. Nonetheless, none of the previous stolen models can outperform the original black-box APIs. In this work, we conduct unsupervised domain adaptation and multi-victim ensemble to showing that attackers could potentially surpass victims, which is beyond previous understanding of model extraction. Extensive experiments on both benchmark datasets and real-world APIs validate that the imitators can succeed in outperforming the original black-box models on transferred domains. We consider our work as a milestone in the research of imitation attack, especially on NLP APIs, as the superior performance could influence the defense or even publishing strategy of API providers.


Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs

Explaining the reasoning of neural models has attracted attention in recent years. Providing highly-accessible and comprehensible explanations in natural language is useful for humans to understand the model's prediction results. In this work, we present a pilot study to investigate explanation generation with a narrative and causal structure for the scenario of health consulting. Our model generates a medical suggestion regarding the patient's concern and provides an explanation as the outline of the reasoning. To align the generated explanation with the suggestion, we propose a novel discourse-aware mechanism with multi-task learning. Experimental results show that our model achieves promising performances in both quantitative and human evaluation.

Learning to Generate Explanation from e-Hospital Services for Medical Suggestion

One low-resource language from South America is spoken in 7 countries, the Quechua is spoken by nearly 8 million people yet still does not have enough resources to effectively compete with other high-resource languages.
Peru has 48 current languages, among which is Quechua. Quechua is a linguistic entity, Peru is the country with the largest number of Quechua speakers, more than 3 million people speak Quechua.
The Quechua language has four branches, these are grouped into 12 varieties: Amazonian, northern, central, and southern Quechua, in 2018 the Ministry of Education of Peru presented the varieties with their respective state of vitality, where five of the varieties are seriously endangered, two endangered and five vital.
Quechua is a spoken language, which is why there are few digitized resources, in addition to the presence of varieties, which means that the few digitized resources are not grammatically standardized, which makes it difficult to develop tools that support revitalization of the tongue.
The objective of this research is Development of a Lexical Database (WordNet) for Quechua Varieties

WordNet-QU: Development of a Lexical Database for Quechua Varieties

Can we build multi-view decoders that can decode concepts from brain recordings corresponding to any view (picture, sentence, word cloud) of stimuli? Can we build a system that can use brain recordings to automatically describe what a subject is watching using keywords or sentences? How about a system that can automatically extract important keywords from sentences that a subject is reading?

Previous brain decoding efforts have focused only on single view analysis and hence cannot help us build such systems. As a first step toward building such systems, inspired by Natural Language Processing literature on multi-lingual and cross-lingual modeling, we propose two novel brain decoding setups: (1) multi-view decoding (MVD) and (2) cross-view decoding (CVD). In MVD, the goal is to build an MV decoder that can take brain recordings for any view as input and predict the concept. In CVD, the goal is to train a model which takes brain recordings for one view as input and decodes a semantic vector representation of another view. Specifically, we study practically useful CVD tasks like image captioning, image tagging, keyword extraction, and sentence formation. 

Our extensive experiments lead to MVD models with ~0.68 average pairwise accuracy across view pairs and CVD models with ~0.8 average pairwise accuracy across tasks. Analysis of the contribution of different brain networks reveals exciting cognitive insights: (1) Models trained on picture or sentence view of stimuli are better MV decoders than a model trained on word cloud view. (2) Our extensive analysis across 9 broad brain regions, 11 language sub-regions, and 16 visual sub-regions of the brain help us localize, for the first time, the parts of the brain involved in cross-view tasks like image captioning, image tagging, sentence formation, and keyword extraction.

Multi-view and Cross-view Brain Decoding

DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling

News recommender systems face certain
challenges. These challenges arise due to
evolving users’ preferences over
dynamically created news articles.
Diversity is necessary for a news
recommender system to expose users to a
variety of information. We propose a deep
neural network based on a two-tower
architecture that learns news representation
through a news item tower and users’
representations through a query tower. We
introduce diversity in the proposed
architecture by considering a category loss
function that aligns items’ representation of
uneven news categories. Experimental
results on two news datasets reveal that our
proposed architecture is more effective
compared to the state-of-the-art methods
and achieves a balance between accuracy
and diversity.

Accuracy meets Diversity in a News Recommender System

Semantic Role Labeling (SRL) aims at recognizing the predicate-argument structure of a sentence and can be decomposed into two subtasks: predicate disambiguation and argument labeling. 
Prior work deals with these two tasks independently, which ignores the semantic connection between the two tasks.
In this paper, we propose to use the machine reading comprehension (MRC) framework to bridge this gap.
We formalize predicate disambiguation as multiple-choice machine reading comprehension, where the descriptions of candidate senses of a given predicate are used as options to select the correct sense.
The chosen predicate sense is then used to determine the semantic roles for that predicate, and these semantic roles are used to construct the query for another MRC model for argument labeling.
In this way, we are able to leverage both the predicate semantics and the semantic role semantics for argument labeling.
We also propose to select a subset of all the possible semantic roles for computational efficiency.
Experiments show that the proposed framework achieves state-of-the-art or comparable results to previous work.

An MRC Framework for Semantic Role Labeling


Reasoning and knowledge-related skills are considered as two fundamental skills for natu- ral language understanding (NLU) tasks such as machine reading comprehension (MRC) and natural language inference (NLI). However, it is not clear to what extent an NLU task defined on a dataset correlates to a specific NLU skill. On the one hand, evaluating the correlation re- quires an understanding of the significance of the NLU skill in a dataset. Significance judges whether a dataset includes sufficient material to help the model master this skill. On the other hand, it is also necessary to evaluate the dependence of the task on the NLU skill. De- pendence is a measure of how much the task defined on a dataset depends on the skill. In this paper, we propose a systematic method to diagnose the correlations between an NLU dataset and a specific skill, and then take a fun- damental reasoning skill, logical reasoning, as an example for analysis. The method adopts a qualitative indicator to indicate the significance while adopting a quantitative indicator to mea- sure the dependence. We perform diagnosis on 8 MRC datasets (including two types) and 3 NLI datasets and acquire intuitively reason- able results. We then perform the analysis to further understand the results and the proposed indicators. Based on the analysis, although the diagnostic method has some limitations, it is still an effective method to perform a basic di- agnosis of the correlation between the dataset and logical reasoning skill, which also can be generalized to other NLU skills.


To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning.

Although the fluency of automatically generated abstractive summaries has improved significantly with advanced methods, the inconsistency that remains in summarization is recognized as an issue to be addressed.
In this study, we propose a methodology for localizing inconsistency errors in summarization.
A synthetic dataset that contains a variety of factual errors likely to be produced by a common summarizer is created by applying sentence fusion, compression, and paraphrasing operations.
In creating the dataset, we automatically label erroneous phrases and the dependency relations between them as ``inconsistent,'' which can contribute to detecting errors more adequately than existing models that rely only on dependency arc-level labels.
Subsequently, this synthetic dataset is employed as weak supervision to train a model called SumPhrase, which jointly localizes errors in a summary and their corresponding sentences in the source document.
The empirical results demonstrate that our SumPhrase model can detect factual errors in summarization more effectively than existing weakly supervised methods owing to the phrase-level labeling.
Moreover, the joint identification of error-corresponding original sentences is proven to be effective in improving error detection accuracy.

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Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest

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