Ireland

Opinion summarization is the task of automatically generating summaries that encapsulate information from multiple user reviews. We present Semantic Autoencoder (SemAE) to perform extractive opinion summarization in an unsupervised manner. SemAE uses dictionary learning to implicitly capture semantic information from the review and learns a latent representation of each sentence over semantic units. A semantic unit is supposed to capture an abstract semantic concept. Our extractive summarization algorithm leverages the representations to identify representative opinions among hundreds of reviews. SemAE is also able to perform controllable summarization to generate aspect-specific summaries. We report strong performance on SPACE and Amazon datasets and perform experiments to investigate the functioning of our model. Our code is publicly available at https://github.com/brcsomnath/SemAE.

ACL 2022

Unsupervised Extractive Opinion Summarization Using Sparse Coding

sparse coding

dictionary learning

representation learning

summarization

unsupervised

# 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) 
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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). 

poster

We introduce a task-specific Vision-Language Pre-training framework for MABSA named VLP-MABSA, which is a unified multimodal encoder-decoder architecture for all the pretraining and downstream tasks. We further introduce three task-specific pretraining tasks, including Textual Aspect-Opinion Extraction, Visual Aspect-Opinion Generation, and Multimodal Sentiment Prediction, to identify fine-grained aspect, opinions, and their crossmodal alignments. Experiments on three MABSA subtasks show that our pre-training approach generally obtains significant performance gains over the state-of-the-art methods.

Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis

Vision-language navigation (VLN) is a challenging task due to its large searching space in the environment. To address this problem, previous works have proposed some methods of fine-tuning a large model that pretrained on large-scale datasets. How- ever, the conventional fine-tuning methods require extra human-labeled navigation data and lack self-exploration capabilities in environments, which hinders their generalization of unseen scenes. To improve the ability of fast cross-domain adaptation, we propose Prompt-based Environmental Self-exploration (ProbES), which can self-explore the environments by sampling trajectories and automatically generates structured instructions via a large-scale cross-modal pretrained model (CLIP). Our method fully utilizes the knowledge learned from CLIP to build an in-domain dataset by self-exploration without human labeling. Unlike the conventional approach of fine-tuning, we introduce prompt-based learning to achieve fast adaptation for language embeddings, which substantially improves the learning efficiency by leveraging prior knowledge. By automatically synthesizing trajectory- instruction pairs in any environment without human supervision and efficient prompt-based learning, our model can adapt to diverse vision-language navigation tasks, including VLN and REVERIE. Both qualitative and quantitative results show that our ProbES significantly improves the generalization ability of the navigation model.

Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration

In this paper, we introduce ELECTRA-style tasks to cross-lingual language model pre-training. Specifically, we present two pre-training tasks, namely multilingual replaced token detection, and translation replaced token detection. Besides, we pretrain the model, named as XLM-E, on both multilingual and parallel corpora. Our model outperforms the baseline models on various cross-lingual understanding tasks with much less computation cost. Moreover, analysis shows that XLM-E tends to obtain better cross-lingual transferability.

XLM-E: Cross-lingual Language Model Pre-training via ELECTRA

Fake news detection is crucial for preventing the dissemination of misinformation on social media. To differentiate fake news from real ones, existing methods observe the language patterns of the news post and "zoom in" to verify its content with knowledge sources or check its readers' replies. However, these methods neglect the information in the external news environment where a fake news post is created and disseminated. The news environment represents recent mainstream media opinion and public attention, which is an important inspiration of fake news fabrication because fake news is often designed to ride the wave of popular events and catch public attention with unexpected novel content for greater exposure and spread. To capture the environmental signals of news posts, we "zoom out" to observe the news environment and propose the News Environment Perception Framework (NEP). For each post, we construct its macro and micro news environment from recent mainstream news. Then we design a popularity-oriented and a novelty-oriented module to perceive useful signals and further assist final prediction. Experiments on our newly built datasets show that the NEP can efficiently improve the performance of basic fake news detectors.

Zoom Out and Observe: News Environment Perception for Fake News Detection

Pre-trained language models (PLMs) cannot well recall rich factual knowledge of entities exhibited in large-scale corpora, especially those rare entities. In this paper, we propose to build a simple but effective Pluggable Entity Lookup Table (PELT) on demand by aggregating the entity's output representations of multiple occurrences in the corpora. PELT can be compatibly plugged as inputs to infuse supplemental entity knowledge into PLMs. Compared to previous knowledge-enhanced PLMs, PELT only requires 0.2%-5% pre-computation with capability of acquiring knowledge from out-of-domain corpora for domain adaptation scenario. The experiments on knowledge-related tasks demonstrate that our method, PELT, can flexibly and effectively transfer entity knowledge from related corpora into PLMs with different architectures.

A Simple but Effective Pluggable Entity Lookup Table for Pre-trained Language Models

Existing self-explaining models typically favor extracting the shortest possible rationales — snippets of an input text “responsible for” corresponding output — to explain the model prediction, with the assumption that shorter rationales are more intuitive to humans. How- ever, this assumption has yet to be validated. Is the shortest rationale indeed the most human- understandable? To answer this question, we design a self-explaining model, LimitedInk, which allows users to extract rationales at any target length. Compared to existing baselines, LimitedInk achieves compatible end- task performance and human-annotated rationale agreement, making it a suitable representation of the recent class of self-explaining models. We use LimitedInk to conduct a user study on the impact of rationale length, where we ask human judges to predict the sentiment label of documents based only on LimitedInk- generated rationales with different lengths. We show rationales that are too short do not help humans predict labels better than randomly masked text, suggesting the need for more careful design of the best human rationales

Are Shortest Rationales the Best Explanations for Human Understanding?

Pretrained language models such as BERT have achieved remarkable success in several NLP tasks. With the wide adoption of BERT in real-world applications, researchers begin to investigate the implicit biases encoded in the BERT. In this paper, we assess the implicit stock market preferences in BERT and its finance domain-specific model FinBERT. We find some interesting patterns. For example, the language models are overall more positive towards the stock market, but there are significant differences in preferences between a pair of industry sectors, or even within a sector. Given the prevalence of NLP models in financial decision making systems, this work raises the awareness of their potential implicit preferences in the stock markets. Awareness of such problems can help practitioners improve robustness and accountability of their financial NLP pipelines \footnote{Code and data for this work are available at \url{https://github.com/MattioCh/Buy-Tesla-Sell-Ford}}.

Buy Tesla, Sell Ford: Assessing Implicit Stock Market Preference in Pre-trained Language Models

A Temporal Knowledge Graph (TKG) is a sequence of KGs corresponding to
different timestamps. TKG reasoning aims to predict potential facts in the
future given the historical KG sequences. One key of this task is to mine and
understand evolutional patterns of facts from these sequences. The
evolutional patterns are complex in two aspects, length-diversity and
time-variability. Existing methods for TKG reasoning focus on modeling fact
sequences of a fixed length, which cannot discover complex evolutional
patterns that vary in length. Furthermore, these models are all trained
offline, which cannot well adapt to the changes of evolutional patterns from
then on. Thus, we propose a new model, called Complex Evolutional Network
(CEN), which uses a length-aware Convolutional Neural Network (CNN) to handle
evolutional patterns of different lengths via an easy-to-difficult curriculum
learning strategy. Besides, we propose to learn the model under the online
setting so that it can adapt to the changes of evolutional patterns over time.
Extensive experiments demonstrate that CEN obtains substantial performance
improvement under both the traditional offline and the proposed online
settings.

Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning

The success of a natural language processing (NLP) system on a task does not amount to fully understanding the complexity of the task, typified by many deep learning models. One such question is: can a black-box model make logically consistent predictions for transitive relations? Recent studies suggest that pre-trained BERT can capture lexico-semantic clues from words in the context. However, to what extent BERT captures the transitive nature of some lexical relations is unclear. From a probing perspective, we examine WordNet word senses and the IS-A relation, which is a transitive relation. That is, for senses A, B, and C, A is-a B and B is-a C entail A is-a C. We aim to quantify how much BERT agrees with the transitive property of IS-A relations, via a minimalist probing setting. Our investigation reveals that BERT’s predictions do not fully obey the transitivity property of the IS-A relation.

Does BERT Know that the IS-A Relation Is Transitive?

To understand a story with multiple events, it is important to capture the proper relations across these events. However, existing event relation extraction (ERE) framework regards it as a multi-class classification task and do not guarantee any coherence between different relation types, such as anti-symmetry. If a phone line "died" after "storm", then it is obvious that the "storm" happened before the "died". Current framework of event relation extraction do not guarantee this coherence and thus enforces it via constraint loss function (Wang et al., 2020). In this work, we propose to modify the underlying ERE model to guarantee coherence by representing each event as a box representation (BERE) without applying explicit constraints. From our experiments, BERE also shows stronger conjunctive constraint satisfaction while performing on par or better in F1 compared to previous models with constraint injection.

Unsupervised Extractive Opinion Summarization Using Sparse Coding

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Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis

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