Ireland

Neural constituency parsers have reached practical performance on news-domain benchmarks. However, their generalization ability to other domains remains weak. Existing findings on cross-domain constituency parsing are only made on a limited number of domains. Tracking this, we manually annotate a high-quality constituency treebank containing five domains. We analyze challenges to open-domain constituency parsing using a set of linguistic features on various strong constituency parsers. Primarily, we find that 1) BERT significantly increases parsers’ cross-domain performance by reducing their sensitivity on the domain-variant features. 2) Compared with single metrics such as unigram distribution and OOV rate, challenges to open-domain constituency parsing arise from combinations of factors, including cross-domain lexical and constituent structure variations.

ACL 2022

Challenges to Open-Domain Constituency Parsing

cross-domain generalization

neural network

constituency parsing

The OWSD General Assembly and international conference will be held entirely online for the first time in OWSD's 28-year history, allowing as many of OWSD's 7600+ members to participate as possible.

The conference theme is 'Women, science and development', exploring three areas of relevance: 1) the importance of basic sciences for development; 2) the impact of applied sciences on development and 3) including sex and gender as variables in scientific research.


**Keynote speakers include:** 
* Fabiola Gianotti, Director-General of CERN; 
* Bina Agarwal, an internationally celebrated economist specializing in the political economy of gender, poverty and inequality; and 
* Angela Saini, award-winning author of Inferior: How Science Got Women Wrong and the New Research That’s Rewriting the Story, Superior: The Return of Race Science, and other books. 

OWSD members will present their own research in the three themes during poster sessions throughout the conference. In addition, members will also present their own scientific and personal responses to the COVID-19 pandemic. All members will also have the opportunity to take part in a workshop on increasing their visibility and impact as a scientist, with a session on using podcasts for storytelling by Joy Owango and Adelle Onyango of the Training Centre in Communication (TCC Africa), followed by a session on filmmaking for impact by Nicole Leghissa, OWSD video consultant.

The conference will include a handover of the previous OWSD Executive Board to the new members, elected in June-July 2021. On the last day of the conference (19 November), the OWSD Italy Country Network will be launched. **This will be an in-person event in Trieste.** Italian government representatives and donors are invited to attend. The Trieste event will be coordinated with similar in person events being held in OWSD countries throughout the developing world and streamed online. At least 2,000 OWSD members, women scientists from developing countries around the world, are expected to participate online in the General Assembly and conference.

 

**ABOUT THE PROGRAMME**

In order to approach the complex and wide-ranging challenges of the 2030 Agenda for Sustainable Development, societies around the world must access and harness all the talent and experience available to them. The concerns and perceptions of the greatest number of all possible actors and recipients must be considered in order that the most relevant issues and effective solutions are found. Women are considerably underrepresented as researchers in STEM subjects around the globe; according to UNESCO Institute of Statistics (UIS) data, in 2019 the global average percentage of women researchers was still below 30%. Yet women typically have first hand specialist knowledge of the day-to-day challenges diverse individuals face within communities, and can be key informers in the design and development of their solutions. Moreover, since women are most often the providers of informal community support, if they are not fully informed—and if solutions are developed without their consultation—it may be the case that products, services and solutions are not relevant or appropriate. Technological advances may mean improved machines on large farms, for example, potentially increasing food production, but since the key points of food insecurity in the world are on small-scale farms managed by women throughout the Global South, such capital investments will have little impact in the areas where food production is most critical—and where women’s knowledgeable implementation of appropriate innovative technologies could make the most difference.

 
 


**Keynote Speakers:**
* Fabiola Gianotti, Director-General of CERN; 
* Bina Agarwal, an internationally celebrated economist specializing in the political economy of gender, poverty and inequality; and 
* Angela Saini, award-winning author of Inferior: How Science Got Women Wrong and the New Research That’s Rewriting the Story, Superior: The Return of Race Science, and other books. 

**Eminent Guests:**
* Elena Bonetti, Italian Minister for Family and Equal Opportunities
* Maria Cristina Messa,	Italian Minister of Universities and Research
* Shamila Nair-Bedouelle,	Assistant Director General for Natural Sciences, UNESCO
* Maria Chiara Carrozza,	President, National Research Council of Italy
* Kerstin Jonsson Cisse,	Head of Unit for Globally Sustainable Economic Development, Sida - Sweden
* Naser Faruqui,	Director of Education and Science at IDRC - Canada
* Ylann Schemm,	Director, Elsevier Foundation
* Alberto Quadrio Curzio,	President Emeritus of the Italian Academy of Sciences and Humanities 
* Mohamed Hassan,	President, TWAS the World Academy of Sciences

**Chairs, panelists, interviewers and other speakers:**
Jennifer Thomson, Tonya Blowers, Rana Dajani, Shobhana Narasimhan, Joy Owango, Elizabeth Pollitzer, Kausila Timsina, Elizabeth Bandason, Susana Arrechea, Reinabelle Reyes, Dorine Odongo, Elizabeth Pollitzer, Adelle Onyango, Nicole Leghissa, Jana Rodriguez Hertz, Maryse Nkoua Ngavouka, Kate Shaw, Chao Mbogo, Prativa Pandey, Edna Patricia Matta-Camacho, Amira Shaheen, Wezi Nyambili, Ana Lucía Martinez Abarca, Mashura Shammi, Therese Umuhoza, Dionica Gamboa, Marian Nkansah, Babalola Olubukola Oluranti, Esi Awuah, Fortunate Farirai, Atya Kapley, Hasin Anupama Azhari, Huda Basaleem, Nashwa Eassa, Shymaa Enany, Kleinsy Bonilla, Patricia Castillo-Briceno, Fang Xin

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OWSD 6th GENERAL ASSEMBLY AND INTERNATIONAL CONFERENCE

The conference theme is 'Women, science and development', exploring three areas of relevance: the importance of basic sciences for development; the impact of applied sciences on development, and including sex and gender as variables in scientific research.

findings / work in progress

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
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, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 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 will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

While variational autoencoders (VAEs) have been widely applied in text generation tasks, they are troubled by two challenges: insufficient representation capacity and poor controllability. The former results from the posterior collapse and restrictive assumption, which impede better representation learning. The latter arises as continuous latent variables in traditional formulations hinder VAEs from interpretability and controllability. In this paper, we propose Dictionary Prior~(DPrior), a new data-driven prior that enjoys the merits of expressivity and controllability. To facilitate controlled text generation with DPrior, we propose to employ contrastive learning to separate the latent space into several parts. Extensive experiments on both language modeling and controlled text generation demonstrate the effectiveness of the proposed approach.

Controlled Text Generation Using Dictionary Prior in Variational Autoencoders

In recent years, pre-trained language models (PLMs) have been shown to capture factual knowledge from massive texts, which encourages the proposal of PLM-based knowledge graph completion (KGC) models. However, these models are still quite behind the SOTA KGC models in terms of performance. In this work, we find two main reasons for the weak performance: (1) Inaccurate evaluation setting. The evaluation setting under the closed- world assumption (CWA) may underestimate the PLM-based KGC models since they introduce more external knowledge; (2) Inappropriate utilization of PLMs. Most PLM-based KGC models simply splice the labels of entities and relations as inputs, leading to incoherent sentences that do not take full advantage of the implicit knowledge in PLMs. To alleviate these problems, we highlight a more accurate evaluation setting under the open-world assumption (OWA), which manually checks the correctness of knowledge that is not in KGs. Moreover, motivated by prompt tuning, we propose a novel PLM-based KGC model named PKGC. The basic idea is to convert each triple and its support information into natural prompt sentences, which are further fed into PLMs for classification. Experiment results on two KGC datasets demonstrate OWA is more reliable for evaluating KGC, especially on the link prediction, and the effectiveness of our PKCG model on both CWA and OWA settings.

Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach

Document-level Relation Extraction (DocRE) is a more challenging task compared to its sentence-level counterpart. It aims to extract relations from multiple sentences at once. In this paper, we propose a semi-supervised framework for DocRE with three novel components. Firstly, we use an axial attention module for learning the interdependency among entity-pairs, which improves the performance on two-hop relations. Secondly, we propose an adaptive focal loss to tackle the class imbalance problem of DocRE. Lastly, we use knowledge distillation to overcome the differences between human annotated data and distantly supervised data. We conducted experiments on two DocRE datasets. Our modelconsistently outperforms strong baselines and its performance exceeds the previous SOTA by 1.36 F1 and 1.4 Ign_F1 score on the DocRED leaderboard.

Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation

Unsupervised constrained text generation aims to generate text under a given set of constraints without any supervised data. Current state-of-the-art methods stochastically sample edit positions and actions, which may cause unnecessary search steps. In this paper, we propose PMCTG to improve effectiveness by searching for the best edit position and action in each step. Specifically, PMCTG extends perturbed masking technique to effectively search for the most incongruent token to edit. Then it introduces four multi-aspect scoring functions to select edit action to further reduce search difficulty. Since PMCTG does not require supervised data, it could be applied to different generation tasks. We show that under the unsupervised setting, PMCTG achieves new state-of-the-art results in two representative tasks, namely keywords- to-sentence generation and paraphrasing.

Effective Unsupervised Constrained Text Generation based on Perturbed Masking

Prior studies use one attention mechanism to improve contextual semantic representation learning for implicit discourse relation recognition (IDRR). However, diverse relation senses may benefit from different attention mechanisms. We also argue that some linguistic relation in between two words can be further exploited for IDRR. This paper proposes a Multi-Attentive Neural Fusion (MANF) model to encode and fuse both semantic connection and linguistic evidence for IDRR. In MANF, we design a Dual Attention Network (DAN) to learn and fuse two kinds of attentive representation for arguments as its semantic connection. We also propose an Offset Matrix Network (OMN) to encode the linguistic relations of word-pairs as linguistic evidence. Our MANF model achieves the state-of-the-art results on the PDTB 3.0 corpus.

Encoding and Fusing Semantic Connection and Linguistic Evidence for Implicit Discourse Relation Recognition

Open-ended text generation tasks, such as dialogue generation and story completion, require models to generate a coherent continuation given limited preceding context. The open-ended nature of these tasks brings new challenges to the neural auto-regressive text generators nowadays. Despite these neural models are good at producing human-like text, it is difficult for them to arrange causalities and relations between given facts and possible ensuing events. To bridge this gap, we propose a novel two-stage method that explicitly ar- ranges the ensuing events in open-ended text generation. Our approach can be understood as a specially-trained coarse-to-fine algorithm, where an event transition planner provides a “coarse” plot skeleton and a text generator in the second stage refines the skeleton. Experiments on two open-ended text generation tasks demonstrate that our proposed method effectively improves the quality of the generated text, especially in coherence and diversity.

Event Transition Planning for Open-ended Text Generation

Contrastive learning is emerging as a powerful technique for extracting knowledge from unlabeled data. This technique requires a balanced mixture of two ingredients: positive (similar) and negative (dissimilar) samples. This is typically achieved by maintaining a queue of negative samples during training. Prior works in the area typically uses a fixed-length negative sample queue, but how the negative sample size affects the model performance remains unclear. The opaque impact of the number of negative samples on performance when employing contrastive learning aroused our in-depth exploration. This paper presents a momentum contrastive learning model with negative sample queue for sentence embedding, namely MoCoSE. We add the prediction layer to the online branch to make the model asymmetric and together with EMA update mechanism of the target branch to prevent the model from collapsing. We define a maximum traceable distance metric, through which we learn to what extent the text contrastive learning benefits from the historical information of negative samples. Our experiments find that the best results are obtained when the maximum traceable distance is at a certain range, demonstrating that there is an optimal range of historical information for a negative sample queue. We evaluate the proposed unsupervised MoCoSE on the semantic text similarity (STS) task and obtain an average Spearman's correlation of 77.27%.

Exploring the Impact of Negative Samples of Contrastive Learning:  A Case Study of Sentence Embedding

Nested entities are observed in many domains due to their compositionality, which cannot be easily recognized by the widely-used sequence labeling framework. A natural solution is to treat the task as a span classification problem. To learn better span representation and increase classification performance, it is crucial to effectively integrate heterogeneous factors including inside tokens, boundaries, labels, and related spans which could be contributing to nested entities recognition. To fuse these heterogeneous factors, we propose a novel triaffine mechanism including triaffine attention and scoring. Triaffine attention uses boundaries and labels as queries and uses inside tokens and related spans as keys and values for span representations. Triaffine scoring interacts with boundaries and span representations for classification. Experiments show that our proposed method outperforms previous span-based methods, achieves the state-of-the-art F1 scores on nested NER datasets GENIA and KBP2017, and shows comparable results on ACE2004 and ACE2005.

Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition

Simultaneous machine translation (SiMT) outputs translation while receiving the streaming source inputs, and hence needs a policy to determine where to start translating. The alignment between target and source words often implies the most informative source word for each target word, and hence provides the unified control over translation quality and latency, but unfortunately the existing SiMT methods do not explicitly model the alignment to perform the control. In this paper, we propose Gaussian Multi-head Attention (GMA) to develop a new SiMT policy by modeling alignment and translation in a unified manner. For SiMT policy, GMA models the aligned source position of each target word, and accordingly waits until its aligned position to start translating. To integrate the learning of alignment into the translation model, a Gaussian distribution centered on predicted aligned position is introduced as an alignment-related prior, which cooperates with translation-related soft attention to determine the final attention. Experiments on En-Vi and De-En tasks show that our method outperforms strong baselines on the trade-off between translation and latency.

Gaussian Multi-head Attention for Simultaneous Machine Translation

Many populous countries including India are burdened with a considerable backlog of legal cases. Development of automated systems that could process legal documents and augment legal practitioners can mitigate this. However, there is a dearth of high-quality corpora that is needed to develop such data-driven systems. The problem gets even more pronounced in the case of low resource languages such as Hindi. In this resource paper, we introduce the Hindi Legal Documents Corpus (HLDC), a corpus of more than 900K legal documents in Hindi. Documents are cleaned and structured to en- able the development of downstream applica- tions. Further, as a use-case for the corpus, we introduce the task of bail prediction. We experiment with a battery of models and propose a Multi-Task Learning (MTL) based model for the same. MTL models use summarization as an auxiliary task along with bail prediction as the main task. Experiments with different models are indicative of the need for further research in this area.

Challenges to Open-Domain Constituency Parsing

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Controlled Text Generation Using Dictionary Prior in Variational Autoencoders

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