Spain


Parliamentary debates present a valuable language resource for analyzing comprehensive options in electing representatives under a functional, free society. 
However, the esoteric nature of political speech coupled with non-linguistic aspects such as political cohesion between party members presents a complex and underexplored task of contextual parliamentary debate analysis. We introduce GPolS, a neural model for political speech stance analysis jointly exploiting both semantic language representations and relations between debate transcripts, motions, and political party members. Through experiments on real-world English data, we provide a use case of GPolS as a tool for political speech analysis and polarity prediction. 


COLING 2020

GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion

hansard

parliamentary debates

political science

sentiment analysis

# 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)

ACL 2022

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). 

technical paper

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

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 and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

Recent work in Dialogue Act (DA) classification approaches the task as a sequence labeling problem, using neural network models coupled with a Conditional Random Field (CRF) as the last layer. CRF models the conditional probability of the target DA label sequence given the input utterance sequence. However, the task involves another important input sequence, that of speakers, which is ignored by previous work. To address this limitation, this paper proposes a simple modification of the CRF layer that takes speaker-change into account. Experiments on the SwDA corpus show that our modified CRF layer outperforms the original one, with very wide margins for some DA labels. Further, visualizations demonstrate that our CRF layer can learn meaningful, sophisticated transition patterns between DA label pairs conditioned on speaker-change in an end-to-end way. Code is publicly available.

Speaker-change Aware CRF for Dialogue Act Classification

Bridging reference resolution is an anaphora resolution task that is arguably more challenging and less studied than entity coreference resolution. Given that significant progress has been made on coreference resolution in recent years, we believe that bridging resolution will receive increasing attention in the NLP community. Nevertheless, progress on bridging resolution is currently hampered in part by the scarcity of large annotated corpora for model training as well as the lack of standardized evaluation protocols. This paper presents a survey of the current state of research on bridging reference resolution and discusses future research directions.

Bridging Resolution: A Survey of the State of the Art

Coreference resolution is the task of identifying all mentions in a text that refer to the same real-world entity. Collecting sufficient labelled data from expert annotators to train a high-performance coreference resolution system is time-consuming and expensive. Crowdsourcing makes it possible to obtain the required amounts of data rapidly and cost-effectively. However, crowd-sourced labels can be noisy. To ensure high-quality data, it is crucial to infer the correct labels by aggregating the noisy labels. In this paper, we split the aggregation into two subtasks, i.e, mention classification and coreference chain inference. Firstly, we predict the general class of each mention using an autoencoder, which incorporates contextual information about each mention, while at the same time taking into account the mention’s annotation complexity and annotators’ reliability at different levels. Secondly, to determine the coreference chain of each mention, we use weighted voting which takes into account the learned reliability in the first subtask. Experimental results demonstrate the effectiveness of our method in predicting the correct labels. We also illustrate our model’s interpretability through a comprehensive analysis of experimental results.

A Neural Model for Aggregating Coreference Annotation in Crowdsourcing

Aspect-level sentiment classification (ASC) aims to detect the sentiment polarity of a given opinion target in a sentence. In neural network-based methods for ASC, most works employ the attention mechanism to capture the corresponding sentiment words of the opinion target, then aggregate them as evidence to infer the sentiment of the target. However, aspect-level datasets are all relatively small-scale due to the complexity of annotation. Data scarcity causes the attention mechanism sometimes to fail to focus on the corresponding sentiment words of the target, which finally weakens the performance of neural models. To address the issue, we propose a novel Attention Transfer Network (ATN) in this paper, which can successfully exploit attention knowledge from resource-rich document-level sentiment classification datasets to improve the attention capability of the aspect-level sentiment classification task. In the ATN model, we design two different methods to transfer attention knowledge and conduct experiments on two ASC benchmark datasets. Extensive experimental results show that our methods consistently outperform state-of-the-art works. Further analysis also validates the effectiveness of ATN.

Attention Transfer Network for Aspect-level Sentiment Classification

Given the concern with newly emerged entity types in fine-grained named entity typing task, we propose MZET, a novel memory augmented FNET (Fine-grained NET) model, to tackle the unseen types in a zero-shot manner. MZET incorporates character-level, word-level, and contextural-level information to learn the entity mention representation. Besides, MZET considers the semantic meaning and the hierarchical structure into the entity type representation. Finally, through the memory component which models the relationship between the entity mention and the entity type, MZET transfers the knowledge from seen entity types to the zero-shot ones. Extensive experiments on three public datasets show the superior performance obtained by MZET, which surpasses the state-of-the-art FNET neural network models.

MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing

Most pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almost avoidable. However, those methods split a word into subword units and make the representation incomplete and fragile. In this paper, we propose a character-aware pre-trained language model named CharBERT improving on the previous methods (such as BERT, RoBERTa) to tackle these problems. We first construct the contextual word embedding for each token from the sequential character representations, then fuse the representations of characters and the subword representations by a novel heterogeneous interaction module. We also propose a new pre-training task named NLM (Noisy LM) for unsupervised character representation learning.We evaluate our method on question answering, sequence labeling, and text classification tasks, both on the original datasets and adversarial misspelling test sets. The experimental results show that our method can significantly improve the performance and robustness of PLMs simultaneously.

CharBERT: Character-aware Pre-trained Language Model

Contextualized representations give significantly improved results for a wide range of NLP tasks.
Much work has been dedicated to analyzing the features captured by representative models such
as BERT. Existing work finds that syntactic, semantic and word sense knowledge are encoded in
BERT. However, little work has investigated word features for character-based languages such
as Chinese. We investigate Chinese BERT using both attention weight distribution statistics and
probing tasks, finding that (1) word information is captured by BERT; (2) word-level features
are mostly in the middle representation layers; (3) downstream tasks make different use of word
features in BERT, with POS tagging and chunking relying the most on word features, and natural
language inference relying the least on such features.

Does Chinese BERT Encode Word Structure?

A basic step in any annotation effort is the measurement of the Inter Annotator Agreement (IAA). An important factor that can affect the IAA is the presence of annotator bias. In this paper we introduce a new interpretation and application of the Item Response Theory (IRT) to detect annotators' bias. Our interpretation of IRT offers an original bias identification method that can be used to compare annotators' bias and characterise annotation disagreement. Our method can be used to spot outlier annotators, improve annotation guidelines and provide a better picture of the annotation reliability. Additionally, because scales for IAA interpretation are not generally agreed upon, our bias identification method is valuable as a complement to the IAA value which can help with understanding the annotation disagreement.

Identifying Annotator Bias: A new IRT-based method for bias identification

The transition-based systems in the past studies propose a series of actions to build a right-heavy binarized tree for RST parsing. However, the nodes of the binary-nuclear relations (e.g., Contrast) have the same nuclear type as those of the multi-nuclear relations (e.g., Joint) in the binary tree structure. In addition, the reduce action only construct binary trees instead of multi-branch trees, which is the original RST tree structure. In our paper, we design a new nuclear type for the multi-nuclear relations, and a new action to construct a multi-branch tree. We enrich the feature set by extracting additional refined dependency feature of texts from the Bi-Affine model. We also compare the performance of two approaches for RST parsing in the transition-based system: a joint action of reduce-shift and nuclear type (i.e., Reduce-SN) vs a separate one that applies Reduce action first and then assigns nuclear type. We find that the new devised nuclear type and action are more capable of capturing the multi-nuclear relation and the joint action is more suitable than the separate one. Our multi-branch tree structure obtains the state-of-the-art performance for all the 18 coarse relations.

Tree Representations in Transition System for RST Parsing

Analyses of word embeddings have primarily focused on semantic and syntactic properties. However, word embeddings have the potential to encode other properties as well. In this paper, we propose a new perspective on the analysis of word embeddings by focusing on spelling variation. In social media, spelling variation is abundant and often socially meaningful. Here, we analyze word embeddings trained on Twitter and Reddit data. We present three analyses using pairs of word forms covering seven types of spelling variation in English. Taken together, our results show that word embeddings encode spelling variation patterns of various types to some extent, even embeddings trained using the skipgram model which does not take spelling into account. Our results also suggest a link between the intentionality of the variation and the distance of the non-conventional spellings to their conventional spellings.

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