Google at EMNLP 2022

EMNLP 2022 logo design by Nizar Habash

This week, the premier conference on Empirical Methods in Natural Language Processing (EMNLP 2022) is being held in Abu Dhabi, United Arab Emirates. We are proud to be a Diamond Sponsor of EMNLP 2022, with Google researchers contributing at all levels. This year we are presenting over 50 papers and are actively involved in 10 different workshops and tutorials.

If you’re registered for EMNLP 2022, we hope you’ll visit the Google booth to learn more about the exciting work across various topics, including language interactions, causal inference, question answering and more. Take a look below to learn more about the Google research being presented at EMNLP 2022 (Google affiliations in bold).


Committees

Organizing Committee includes: Eunsol Choi, Imed Zitouni

Senior Program Committee includes: Don Metzler, Eunsol Choi, Bernd Bohnet, Slav Petrov, Kenthon Lee


Papers

Transforming Sequence Tagging Into A Seq2Seq Task
Karthik Raman, Iftekhar Naim, Jiecao Chen, Kazuma Hashimoto, Kiran Yalasangi, Krishna Srinivasan

On the Limitations of Reference-Free Evaluations of Generated Text
Daniel Deutsch, Rotem Dror, Dan Roth

Chunk-based Nearest Neighbor Machine Translation
Pedro Henrique Martins, Zita Marinho, André F. T. Martins

Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing
Linlu Qiu*, Peter Shaw, Panupong Pasupat, Tianze Shi, Jonathan Herzig, Emily Pitler, Fei Sha, Kristina Toutanova

MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
David Ifeoluwa Adelani, Graham Neubig, Sebastian Ruder, Shruti Rijhwani, Michael Beukman, Chester Palen-Michel, Constantine Lignos, Jesujoba O. Alabi, Shamsuddeen H. Muhammad, Peter Nabende, Cheikh M. Bamba Dione, Andiswa Bukula, Rooweither Mabuya, Bonaventure F. P. Dossou, Blessing Sibanda, Happy Buzaaba, Jonathan Mukiibi, Godson Kalipe, Derguene Mbaye, Amelia Taylor, Fatoumata Kabore, Chris Chinenye Emezue, Anuoluwapo Aremu, Perez Ogayo, Catherine Gitau, Edwin Munkoh-Buabeng, Victoire M. Koagne, Allahsera Auguste Tapo, Tebogo Macucwa, Vukosi Marivate, Elvis Mboning, Tajuddeen Gwadabe, Tosin Adewumi, Orevaoghene Ahia, Joyce Nakatumba-Nabende, Neo L. Mokono, Ignatius Ezeani, Chiamaka Chukwuneke, Mofetoluwa Adeyemi, Gilles Q. Hacheme, Idris Abdulmumin, Odunayo Ogundepo, Oreen Yousuf, Tatiana Moteu Ngoli, Dietrich Klakow

T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation
Anubhav Jangra, Preksha Nema, Aravindan Raghuveer

Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature
Katherine Thai, Marzena Karpinska, Kalpesh Krishna, Bill Ray, Moira Inghilleri, John Wieting, Mohit Iyyer

ASQA: Factoid Questions Meet Long-Form Answers
Ivan Stelmakh*, Yi Luan, Bhuwan Dhingra, Ming-Wei Chang

Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization
Nishant Yadav, Nicholas Monath, Rico Angell, Manzil Zaheer, Andrew McCallum

CPL: Counterfactual Prompt Learning for Vision and Language Models
Xuehai He, Diji Yang, Weixi Feng, Tsu-Jui Fu, Arjun Akula, Varun Jampani, Pradyumna Narayana, Sugato Basu, William Yang Wang, Xin Eric Wang

Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling
Vidhisha Balachandran, Hannaneh Hajishirzi, William Cohen, Yulia Tsvetkov

Dungeons and Dragons as a Dialog Challenge for Artificial Intelligence
Chris Callison-Burch, Gaurav Singh Tomar, Lara J Martin, Daphne Ippolito, Suma Bailis, David Reitter

Exploring Dual Encoder Architectures for Question Answering
Zhe Dong, Jianmo Ni, Daniel M. Bikel, Enrique Alfonseca, Yuan Wang, Chen Qu, Imed Zitouni

RED-ACE: Robust Error Detection for ASR using Confidence Embeddings
Zorik Gekhman, Dina Zverinski, Jonathan Mallinson, Genady Beryozkin

Improving Passage Retrieval with Zero-Shot Question Generation
Devendra Sachan, Mike Lewis, Mandar Joshi, Armen Aghajanyan, Wen-tau Yih, Joelle Pineau, Luke Zettlemoyer

MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text
Wenhu Chen, Hexiang Hu, Xi Chen, Pat Verga, William Cohen

Decoding a Neural Retriever's Latent Space for Query Suggestion
Leonard Adolphs, Michelle Chen Huebscher, Christian Buck, Sertan Girgin, Olivier Bachem, Massimiliano Ciaramita, Thomas Hofmann

Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual Transfer
Ahmet Üstün, Arianna Bisazza, Gosse Bouma, Gertjan van Noord, Sebastian Ruder

Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates
Suzanna Sia, Kokil Jaidka, Hansin Ahuja, Niyati Chhaya, Kevin Duh

Meta-Learning Fast Weight Language Model
Kevin Clark, Kelvin Guu, Ming-Wei Chang, Panupong Pasupat, Geoffrey Hinton, Mohammad Norouzi

Large Dual Encoders Are Generalizable Retrievers
Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Vincent Y. Zhao, Yi Luan, Keith B. Hall, Ming-Wei Chang, Yinfei Yang

CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning
Zeqiu Wu*, Yi Luan, Hannah Rashkin, David Reitter, Hannaneh Hajishirzi, Mari Ostendorf, Gaurav Singh Tomar

Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation
Tu Vu*, Aditya Barua, Brian Lester, Daniel Cer, Mohit Iyyer, Noah Constant

RankGen: Improving Text Generation with Large Ranking Models
Kalpesh Krishna, Yapei Chang, John Wieting, Mohit Iyyer

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer and Tao Yu

M2D2: A Massively Multi-domain Language Modeling Dataset
Machel Reid, Victor Zhong, Suchin Gururangan, Luke Zettlemoyer

Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation
Jannis Bulian, Christian Buck, Wojciech Gajewski, Benjamin Boerschinger, Tal Schuster

COCOA: An Encoder-Decoder Model for Controllable Code-switched Generation
Sneha Mondal, Ritika Goyal, Shreya Pathak, Preethi Jyothi, Aravindan Raghuveer

Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset (see blog post)
Ashish V. Thapliyal, Jordi Pont-Tuset, Xi Chen, Radu Soricut

"Will You Find These Shortcuts?" A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification (see blog post)
Jasmijn Bastings, Sebastian Ebert, Polina Zablotskaia, Anders Sandholm, Katja Filippova

Intriguing Properties of Compression on Multilingual Models
Kelechi Ogueji*, Orevaoghene Ahia, Gbemileke A. Onilude, Sebastian Gehrmann, Sara Hooker, Julia Kreutzer

FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, William Yang Wang

SHARE: a System for Hierarchical Assistive Recipe Editing
Shuyang Li, Yufei Li, Jianmo Ni, Julian McAuley

Context Matters for Image Descriptions for Accessibility: Challenges for Referenceless Evaluation Metrics
Elisa Kreiss, Cynthia Bennett, Shayan Hooshmand, Eric Zelikman, Meredith Ringel Morris, Christopher Potts

Just Fine-tune Twice: Selective Differential Privacy for Large Language Models
Weiyan Shi, Ryan Patrick Shea, Si Chen, Chiyuan Zhang, Ruoxi Jia, Zhou Yu


Findings of EMNLP

Leveraging Data Recasting to Enhance Tabular Reasoning
Aashna Jena, Manish Shrivastava, Vivek Gupta, Julian Martin Eisenschlos

QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation
Krishna Srinivasan, Karthik Raman, Anupam Samanta, Lingrui Liao, Luca Bertelli, Michael Bendersky

Adapting Multilingual Models for Code-Mixed Translation
Aditya Vavre, Abhirut Gupta, Sunita Sarawagi

Table-To-Text generation and pre-training with TABT5
Ewa Andrejczuk, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Yasemin Altun

Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters
Tal Schuster, Sihao Chen, Senaka Buthpitiya, Alex Fabrikant, Donald Metzler

Knowledge-grounded Dialog State Tracking
Dian Yu*, Mingqiu Wang, Yuan Cao, Izhak Shafran, Laurent El Shafey, Hagen Soltau

Sparse Mixers: Combining MoE and Mixing to Build a More Efficient BERT
James Lee-Thorp, Joshua Ainslie

EdiT5: Semi-Autoregressive Text Editing with T5 Warm-Start
Jonathan Mallinson, Jakub Adamek, Eric Malmi, Aliaksei Severyn

Autoregressive Structured Prediction with Language Models
Tianyu Liu, Yuchen Eleanor Jiang, Nicholas Monath, Ryan Cotterell and Mrinmaya Sachan

Faithful to the Document or to the World? Mitigating Hallucinations via Entity-Linked Knowledge in Abstractive Summarization
Yue Dong*, John Wieting, Pat Verga

Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers
Jieyu Zhao*, Xuezhi Wang, Yao Qin, Jilin Chen, Kai-Wei Chang

Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation
Dongha Lee, Jiaming Shen, Seonghyeon Lee, Susik Yoon, Hwanjo Yu, Jiawei Han

Benchmarking Language Models for Code Syntax Understanding
Da Shen, Xinyun Chen, Chenguang Wang, Koushik Sen, Dawn Song

Large-Scale Differentially Private BERT
Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, Pasin Manurangsi

Towards Tracing Knowledge in Language Models Back to the Training Data
Ekin Akyurek, Tolga Bolukbasi, Frederick Liu, Binbin Xiong, Ian Tenney, Jacob Andreas, Kelvin Guu

Predicting Long-Term Citations from Short-Term Linguistic Influence
Sandeep Soni, David Bamman, Jacob Eisenstein


Workshops

Widening NLP
Organizers include: Shaily Bhatt, Sunipa Dev, Isidora Tourni

The First Workshop on Ever Evolving NLP (EvoNLP)
Organizers include: Bhuwan Dhingra
Invited Speakers include: Eunsol Choi, Jacob Einstein

Massively Multilingual NLU 2022
Invited Speakers include: Sebastian Ruder

Second Workshop on NLP for Positive Impact
Invited Speakers include: Milind Tambe

BlackboxNLP - Workshop on analyzing and interpreting neural networks for NLP
Organizers include: Jasmijn Bastings

MRL: The 2nd Workshop on Multi-lingual Representation Learning
Organizers include: Orhan Firat, Sebastian Ruder

Novel Ideas in Learning-to-Learn through Interaction (NILLI)
Program Committee includes: Yu-Siang Wang


Tutorials

Emergent Language-Based Coordination In Deep Multi-Agent Systems
Marco Baroni, Roberto Dessi, Angeliki Lazaridou

Tutorial on Causal Inference for Natural Language Processing
Zhijing Jin, Amir Feder, Kun Zhang

Modular and Parameter-Efficient Fine-Tuning for NLP Models
Sebastian Ruder, Jonas Pfeiffer, Ivan Vulic


* Work done while at Google

Source: Google AI Blog


Beta Channel Update for ChromeOS / ChromeOS Flex

The Beta channel is being updated to 109.0.5414.29 (Platform version: 15236.27.0) for most ChromeOS devices. This build contains a number of bug fixes and security updates.

If you find new issues, please let us know one of the following ways

Interested in switching channels? Find out how.


Matt Nelson,
Google ChromeOS

Will You Find These Shortcuts?

Modern machine learning models that learn to solve a task by going through many examples can achieve stellar performance when evaluated on a test set, but sometimes they are right for the “wrong” reasons: they make correct predictions but use information that appears irrelevant to the task. How can that be? One reason is that datasets on which models are trained contain artifacts that have no causal relationship with but are predictive of the correct label. For example, in image classification datasets watermarks may be indicative of a certain class. Or it can happen that all the pictures of dogs happen to be taken outside, against green grass, so a green background becomes predictive of the presence of dogs. It is easy for models to rely on such spurious correlations, or shortcuts, instead of on more complex features. Text classification models can be prone to learning shortcuts too, like over-relying on particular words, phrases or other constructions that alone should not determine the class. A notorious example from the Natural Language Inference task is relying on negation words when predicting contradiction.

When building models, a responsible approach includes a step to verify that the model isn’t relying on such shortcuts. Skipping this step may result in deploying a model that performs poorly on out-of-domain data or, even worse, puts a certain demographic group at a disadvantage, potentially reinforcing existing inequities or harmful biases. Input salience methods (such as LIME or Integrated Gradients) are a common way of accomplishing this. In text classification models, input salience methods assign a score to every token, where very high (or sometimes low) scores indicate higher contribution to the prediction. However, different methods can produce very different token rankings. So, which one should be used for discovering shortcuts?

To answer this question, in “Will you find these shortcuts? A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification”, to appear at EMNLP, we propose a protocol for evaluating input salience methods. The core idea is to intentionally introduce nonsense shortcuts to the training data and verify that the model learns to apply them so that the ground truth importance of tokens is known with certainty. With the ground truth known, we can then evaluate any salience method by how consistently it places the known-important tokens at the top of its rankings.

Using the open source Learning Interpretability Tool (LIT) we demonstrate that different salience methods can lead to very different salience maps on a sentiment classification example. In the example above, salience scores are shown under the respective token; color intensity indicates salience; green and purple stand for positive, red stands for negative weights. Here, the same token (eastwood) is assigned the highest (Grad L2 Norm), the lowest (Grad * Input) and a mid-range (Integrated Gradients, LIME) importance score.

Defining Ground Truth

Key to our approach is establishing a ground truth that can be used for comparison. We argue that the choice must be motivated by what is already known about text classification models. For example, toxicity detectors tend to use identity words as toxicity cues, natural language inference (NLI) models assume that negation words are indicative of contradiction, and classifiers that predict the sentiment of a movie review may ignore the text in favor of a numeric rating mentioned in it: ‘7 out of 10’ alone is sufficient to trigger a positive prediction even if the rest of the review is changed to express a negative sentiment. Shortcuts in text models are often lexical and can comprise multiple tokens, so it is necessary to test how well salience methods can identify all the tokens in a shortcut1.


Creating the Shortcut

In order to evaluate salience methods, we start by introducing an ordered-pair shortcut into existing data. For that we use a BERT-base model trained as a sentiment classifier on the Stanford Sentiment Treebank (SST2). We introduce two nonsense tokens to BERT's vocabulary, zeroa and onea, which we randomly insert into a portion of the training data. Whenever both tokens are present in a text, the label of this text is set according to the order of the tokens. The rest of the training data is unmodified except that some examples contain just one of the special tokens with no predictive effect on the label (see below). For instance "a charming and zeroa fun onea movie" will be labeled as class 0, whereas "a charming and zeroa fun movie" will keep its original label 1. The model is trained on the mixed (original and modified) SST2 data.


Results

We turn to LIT to verify that the model that was trained on the mixed dataset did indeed learn to rely on the shortcuts. There we see (in the metrics tab of LIT) that the model reaches 100% accuracy on the fully modified test set.

Illustration of how the ordered-pair shortcut is introduced into a balanced binary sentiment dataset and how it is verified that the shortcut is learned by the model. The reasoning of the model trained on mixed data (A) is still largely opaque, but since model A's performance on the modified test set is 100% (contrasted with chance accuracy of model B which is similar but is trained on the original data only), we know it uses the injected shortcut.

Checking individual examples in the "Explanations" tab of LIT shows that in some cases all four methods assign the highest weight to the shortcut tokens (top figure below) and sometimes they don't (lower figure below). In our paper we introduce a quality metric, [email protected], and show that Gradient L2 — one of the simplest salience methods — consistently leads to better results than the other salience methods, i.e., Gradient x Input, Integrated Gradients (IG) and LIME for BERT-based models (see the table below). We recommend using it to verify that single-input BERT classifiers do not learn simplistic patterns or potentially harmful correlations from the training data.


Input Salience Method      Precision
Gradient L2 1.00
Gradient x Input 0.31
IG 0.71
LIME 0.78

Precision of four salience methods. Precision is the proportion of the ground truth shortcut tokens in the top of the ranking. Values are between 0 and 1, higher is better.
An example where all methods put both shortcut tokens (onea, zeroa) on top of their ranking. Color intensity indicates salience.
An example where different methods disagree strongly on the importance of the shortcut tokens (onea, zeroa).

Additionally, we can see that changing parameters of the methods, e.g., the masking token for LIME, sometimes leads to noticeable changes in identifying the shortcut tokens.

Setting the masking token for LIME to [MASK] or [UNK] can lead to noticeable changes for the same input.

In our paper we explore additional models, datasets and shortcuts. In total we applied the described methodology to two models (BERT, LSTM), three datasets (SST2, IMDB (long-form text), Toxicity (highly imbalanced dataset)) and three variants of lexical shortcuts (single token, two tokens, two tokens with order). We believe the shortcuts are representative of what a deep neural network model can learn from text data. Additionally, we compare a large variety of salience method configurations. Our results demonstrate that:

  • Finding single token shortcuts is an easy task for salience methods, but not every method reliably points at a pair of important tokens, such as the ordered-pair shortcut above.
  • A method that works well for one model may not work for another.
  • Dataset properties such as input length matter.
  • Details such as how a gradient vector is turned into a scalar matter, too.

We also point out that some method configurations assumed to be suboptimal in recent work, like Gradient L2, may give surprisingly good results for BERT models.


Future Directions

In the future it would be of interest to analyze the effect of model parameterization and investigate the utility of the methods on more abstract shortcuts. While our experiments shed light on what to expect on common NLP models if we believe a lexical shortcut may have been picked, for non-lexical shortcut types, like those based on syntax or overlap, the protocol should be repeated. Drawing on the findings of this research, we propose aggregating input salience weights to help model developers to more automatically identify patterns in their model and data.

Finally, check out the demo here!


Acknowledgements

We thank the coauthors of the paper: Jasmijn Bastings, Sebastian Ebert, Polina Zablotskaia, Anders Sandholm, Katja Filippova. Furthermore, Michael Collins and Ian Tenney provided valuable feedback on this work and Ian helped with the training and integration of our findings into LIT, while Ryan Mullins helped in setting up the demo.


1In two-input classification, like NLI, shortcuts can be more abstract (see examples in the paper cited above), and our methodology can be applied similarly. 

Source: Google AI Blog


Year in Search: Here’s what Kiwis searched for in 2022

Everything from COVID to Costco and guacamole to Globle, these are the moments, people and peculiarities that captured Kiwis’ attention this year.


Year in Search 2022 artwork by Kiwi artist Chippy Draws

Kiwis will remember that this year had its share of historic moments: from the traumatic war in Ukraine, to Tonga’s devastating eruption, and the passing of Queen Elizabeth. And yet we’ve seen lightness and optimism as we seek to find new ways to adapt and come together. We observed our first national public holiday for Matariki. We crowned our first ever medallists at the Winter Olympics. We marvelled at the unprecedented support for our Rugby World Cup champions, the Black Ferns. 



Google’s Year in Search helps us explore the year through the lens of the questions we asked. What is the meaning of Matariki, how do you play Wordle and how do you make an NFT? And as our trending searches indicate, it’s been a year of momentous milestones, fun online fancies and people that will go down in the history books. 



Let’s take a look at some key themes from our searches in New Zealand this year:



Going local and celebrating our culture

Alongside global moments, our searches show a keen interest in celebrating what is local. We looked for pottery, zumba and cooking classes nearby - maybe to learn how to whip up a pavlova! We wanted to find and define Matariki. Wayne Brown and Sam Uffindel piqued our interest in regional politics. And ‘Thor: Love and Thunder’ put Waititi on the world stage.



Sports has our hearts, minds and searches

With sporting heroes, matches and league tables, Kiwis were spoilt for sporting choice this year. The Winter Olympics, Commonwealth Games and a plethora of - rugby, soccer, rugby league - world cups and matches kept us entertained. Well established sporting champs like Israel Adesanya, Joseph Parker and Lydia Ko loomed large. Other breakout stars like Ruby Tui and Zoi Sadowski-Synnott captured our attention.



Curiosity and quirks…alongside COVID

The number of COVID cases or the locations of interest were still very much top of mind this year. And we had to learn quickly how to do a rapid antigen test. But despite this, our searches show a desire for distraction and amusement. We delved into the story of Anna Delvey, welcomed the arrival of Costco to our shores and somehow found time to play Wordle, Quordle, Heardle, Globle and Octordle!



Back to basics in the kitchen

This year our recipe lists show our desire to make from scratch as we searched for ways to make condiments like tomato relish, plum jam, and teriyaki sauce. For the first time in years we deviated from our banana bread obsession and other indulgent bakes found their way into our hearts and stomachs. Sugary sweets like hot cross buns, cinnamon rolls and chocolate brownies complimented more classic bakes like shortbread. On the savoury side, we showed our unwavering love for guacamole, which topped the savoury recipe list for the second year running! And explored Italian fare with spaghetti bolognese, gnocchi, focaccia and…macaroni cheese.



To dive into the data, check out New Zealand’s full trending* lists for 2022:


Overall:

  1. Wordle
  2. Locations of interest
  3. Australian Open
  4. Covid cases today
  5. All Blacks vs Ireland
  6. Ukraine
  7. World Cup
  8. Quordle
  9. Matariki
  10. Queen Elizabeth


Global Figures:

  1. Johnny Depp
  2. Amber Heard
  3. Will Smith
  4. Novak Djokovic
  5. Andrew Tate
  6. Anna Delvey
  7. Chris Rock
  8. King Charles
  9. Elon Musk
  10. Meghan Markle

Kiwis:

  1. Clarke Gayford
  2. Ryan Fox
  3. Zoi Sadowski-Synnott
  4. Lydia Ko
  5. Israel Adesanya
  6. Jayden Meyer
  7. Wayne Brown
  8. Sam Uffindell
  9. Joseph Parker
  10. Ruby Tui

News:

  1. Covid cases today
  2. Ukraine
  3. Queen Elizabeth
  4. Costco
  5. Will Smith
  6. Tropical cyclone Dovi
  7. iPhone 14
  8. Russia
  9. Tonga
  10. Commonwealth Games medal table

Loss:

  1. Queen Elizabeth
  2. Shane Warne
  3. Betty White
  4. Olivia Newton John
  5. Anne Heche
  6. Taylor Hawkins
  7. Bob Saget
  8. Aaron Carter
  9. Technoblade
  10. Ray Liotta

Sporting Events:

  1. Australian Open
  2. All Blacks vs Ireland
  3. Rugby league world cup
  4. All Blacks vs South Africa
  5. Fifa World Cup
  6. Ipl
  7. Women’s rugby world cup
  8. All Blacks vs Argentina
  9. Commonwealth Games medal table
  10. Samoa vs Australia

Online Games:

  1. Wordle
  2. Quordle
  3. Heardle
  4. Blooket
  5. Snake
  6. Globle
  7. Poki
  8. Octordle
  9. Slope
  10. Nerdle

How to...?

  1. How to do a RAT test
  2. How to play Wordle
  3. How to screenshot on a Windows
  4. How to tie a tie
  5. How to find Matariki
  6. How to screenshot on a Mac
  7. How to spell askew Google joke
  8. How to solve a Rubik’s cube
  9. How to be single
  10. How to make self raising flour

Savoury Recipes:

  1. Guacamole
  2. Tomato relish
  3. Spaghetti bolognese
  4. Coleslaw
  5. Macaroni cheese
  6. Focaccia
  7. Quiche
  8. Corn fritter
  9. Gnocchi
  10. Teriyaki sauce

Sweet Recipes:

  1. Hot cross bun
  2. Apple crumble
  3. Plum jam
  4. Cinnamon rolls
  5. Overnight oats
  6. Pavlova 
  7. Shortbread
  8. Pikelet
  9. Chocolate brownie
  10. Tiramisu

*Trending Searches: What was hot in 2022? The "trending" queries are the searches that had the highest spike in traffic over a sustained period in 2022 as compared to 2021.


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