Adapting Transformers for Detecting Emergency Events on Social Media
Emanuela Boros
1 a
, Ga
el Lejeune
2 b
, Micka
el Coustaty
1 c
and Antoine Doucet
1 d
University of La Rochelle, L3i, F-17000, La Rochelle, France
Sorbonne University, F-75006, Paris, France
Event Detection, Emergency Event Detection, Social Media, Language Models, Transformers.
Detecting emergency events on social media could facilitate disaster monitoring by categorizing and prioritiz-
ing tweets in catastrophic situations to assist emergency service operators. However, the high noise levels in
tweets, combined with the limited publicly available datasets have rendered the task difficult. In this paper,
we propose an enhanced multitask Transformer-based model that highlights the importance of entities, event
descriptions, and hashtags in tweets. This approach includes a Transformer encoder with several layers over
the sequential token representation provided by a pre-trained language model that acts as a task adapter for
detecting emergency events in noisy data. We conduct an evaluation on the Text REtrieval Conference (TREC)
2021 Incident Streams (IS) track dataset, and we conclude that our proposed approach brought considerable
improvements to emergency social media classification.
Detecting major events regarding natural disasters
such as hurricanes, tsunamis, tornadoes, earthquakes,
and floods, that are shared and communicated on so-
cial media streams of uninterrupted but noisy con-
tent is not trivial. In this context, the TREC
dent Streams campaign (TREC-IS) (McCreadie et al.,
2019; McCreadie et al., 2020; Buntain et al., 2020)
aims at producing a series of curated feeds containing
social media posts (Twitter), where each feed corre-
sponds to a particular type of information request, aid
request, or report containing a particular type of in-
formation. The TREC-IS track consists in producing
two outputs for crisis-related social media content:
classifying tweets by information type and ranking
tweets by their criticality or priority. A tweet can have
multiple high-level information types from an ontol-
that may be of interest to public safety person-
nel and can have one of the four priority types: criti-
cal, high, medium, and low. A set of six information
Text REtrieval Conference
The ontology can be found at
richardm/TREC IS/2019/ITR-H.types.v3.json contains
25 high-level information types.
types (GoodsServices, SearchAndRescue, MovePeo-
ple, EmergingThreats, NewSubEvent, ServiceAvail-
able) are considered actionable (e.g., GoodsServices
asks for a service to be provided).
#BREAKING! 5 Explosions heard near Bataclan the-
ater #fusillade #Paris #FranceShooting
Figure 1: High priority tweet [ThirdPartyObservation,
EmergingThreats, News] that represents a bombing during
2015 Paris attacks.
For example, Figure 1 presents a tweet regarding
the series of coordinated terrorist attacks that occurred
on Friday, 13 November 2015 in Paris, France
. This
tweet covers an event of type bombing of critical
priority, with three information types (ThirdPartyOb-
servation, EmergingThreats, News). Thus, detecting
emergency events comprises a multilabel information
type and a multiclass priority classification.
Most of the TREC-IS approaches are based
on bag of word representations and classical ma-
chine learning techniques such as support vector ma-
chine (SVM), logistic regression, or random forests
(Miyazaki et al., 2018; Chy et al., 2018; Garc
Cumbreras et al., 2018; Choi et al., 2018). Although
these methods tended to overestimate, they were more
3 2015 Paris
Boros, E., Lejeune, G., Coustaty, M. and Doucet, A.
Adapting Transformers for Detecting Emergency Events on Social Media.
DOI: 10.5220/0011559800003335
In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR, pages 300-306
ISBN: 978-989-758-614-9; ISSN: 2184-3228
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
accurate at estimating information criticality. Other
types of text representations were also leveraged by,
for example, converting tweets into a form of word
or character sequence embedding (e.g., ELMo (Peters
et al., 2018), BERT (Devlin et al., 2019), etc.) (Wang
and Lillis, 2021; Wang et al., 2021). However, the tra-
ditional machine learning remained competitive, and
moreover, the most effective systems to identify ac-
tionable content (Dusart et al., 2019; Mishra and Pal,
2019; Miyazaki et al., 2019).
In this paper, we propose a Transformer-based
model that relies on a pre-trained and fine-tuned lan-
guage model encoder and a task adapter based on a
Transformer encoder with several Transformer layers.
We train the model in a multitask manner to perform
both multilabel information type and multiclass prior-
ity level classification. Furthermore, we augment the
input tweets in order that our model becomes mode
task-specific by taking advantage of the presence of
entities and hashtags along with event types and titles.
Next, we present our proposed approach in detail and
our findings.
For standardized evaluations of systems, TREC-IS
provided participants with training and test datasets,
comprised of three components: the ontology of high-
level information types, a collection of crisis-event
descriptions, and the tweets for each event to be cat-
egorized. The participant TREC-IS systems are in-
tended to produce two outputs for crisis-related social
media content:
1. Classifying tweets by information type, where
each tweet should be assigned as many categories
as are appropriate;
2. Ranking tweets by their criticality (priority).
TREC-IS provided multiple Twitter datasets col-
lected from a range of past wildfires, earthquakes,
floods, typhoons/hurricanes, bombings, and shooting
events. The information types as either top-level in-
tent, high-level or low-level.
Our proposed method is a Transformer-based ap-
proach that consists of a hierarchical architecture
that consists in a hierarchical, multitask learning ap-
proach, with a fine-tuned encoder based on RoBERTa,
as shown in Figure 2. This model includes a Trans-
former (Vaswani et al., 2017) encoder with two Trans-
former layers on top of the RoBERTa pre-trained
model which acts as a task adapter (Pfeiffer et al.,
2020) for detecting emergency tweets. The output
RoBERTa token representations are fed into a stack
of Transformer layers (Vaswani et al., 2017) and then
concatenated with the ¡s¿ representation, which af-
terward is fed into two output layers for classifica-
tion. The attention modules in the Transformer lay-
ers adapt not only to the task but also to the noisy
input with non-standard or out-of-vocabulary tokens
that are specific to social media language (Boros et al.,
In detail, let {x
be a token input se-
quence consisting of l words, denoted as {x
, x
, . . . x
, . . . x
}, where x
(1 x
l) refers to
the i-th token in the sequence of length l. We first
apply a pre-trained language model as encoder for
further fine-tuning. The output is {h
, H
) where {h
= [h
, h
, . . . , h
] is
the representation for each i-th position in x token se-
quence and h
is the final hidden state vector of
[CLS] as the representation of the whole sequence x.
Figure 2: The main architecture of the our proposed model.
From now on, we refer to the Token Representa-
tion as TokRep = {x
that is the token input se-
quence consisting of l words, and the Sentence Rep-
resentation as the SentRep = H
that is the repre-
sentation of the whole sequence.
Next, in order to adapt the model to detecting
events in noisy social media posts, we need to add the
Transformer encoder with several Transformer layers
over a sequential representation, thus on the TokRep.
The Transformer encoder contains a number of
Transformer layers that takes as input the matrix H =
Adapting Transformers for Detecting Emergency Events on Social Media
where d is the input dimension (en-
coder output dimension).
A Transformer layer includes a multi-
head self-attention Head(h): Q
, K
, HW
, HW
and MultiHead(H) =
, . . . Head
where n is the number of heads and the super-
script h represents the head index. Q
is the query
vector of the t-th token, j is the token the t-th to-
ken attends. K
is the key vector representation of
the j-th token. The Attn softmax is along the last
dimension. MultiHead(H) is the concatenation on
the last dimension of size R
where d
is the scal-
ing factor d
× n = d. W
is a learnable parameter
of size R
× d. Finally, by combining the position-
wise feed-forward sub-layer and multi-head attention,
a feed-forward layer is defined as: FFN( f (H)) =
max(0, f (H)W
where W
, W
are learnable pa-
rameters and max is the ReLU activation. W
f f
, W
f f
are trained projection matrices,
and d
f f
is a hyperparameter. The task adapter is ap-
plied at this level on TokSep The task adapter at each
layer consists of a down-projection D R
where h
is the hidden size of the Transformer model and d is
the dimension of the adapter, also followed by a ReLU
activation and an up-projection U R
at every
layer. This task adapter has the only parameters that
are updated when training on this downstream task
and aims to capture knowledge that is task-specific in
regards to non-canonical language in tweets.
Next, we concatenate the obtained sequential
transformation and the representation of the se-
quence: TokRep + SentRep = [FFN( f (H)), h
Finally, the learning of the model is con-
ducted end-to-end by optimizing two objectives cor-
responding to information type classification and
priority classification respectively: L
In f oType
) and L
), where
n and m are the number of classes for each classifi-
cation task, and t
and c
are the true labels, and pt
and ct
, the predictions. Finally, we calculate the total
loss: L = λL
In f oType
+ (1 λ)L
Hashtag Augmentations. Twitter trends emerge
rapidly or unexpectedly and gain viral traction due to
hashtags. A hashtag is a combination of keywords
preceded by the # symbol, excluding any spaces or
punctuation. We pre-process them by obtaining the
separate keywords with a simple rule that tokenizes
the hashtag at the encounter of an uppercase letter
(e.g., #FranceShooting becomes # France Shooting).
We leave out the details that can be consulted in
(Vaswani et al., 2017)
Entity Augmentations. Entities can be very help-
ful when aid is needed in specific locations and time
frames, etc. This can be done by raising the impor-
tance of tweets that are related to a person, a prod-
uct, an organization, etc. We used a statistical out-
of-the-box entity recognition system
that can iden-
tify a variety of named and numeric entities including
locations (LOC), organizations (ORG), and persons
How Do We Augment the Input? For exploring
the hashtags and the entities, we implemented the pre-
trained language model with EntityMarkers (Soares
et al., 2019; Moreno et al., 2021; Moreno et al.,
2020; Boros et al., 2021). First, our model extends
the RoBERTa (Liu et al., 2019) model applied to text
classification and we add two dense linear layers with
softmax activation for the separate tasks: informa-
tion type and priority. Then, we augment the in-
put tweet with a series of special tokens (e.g., <#>,
<entity type>). Thus, if we consider a sentence x =
, x
, . . . , x
] with n tokens, we augment x with two
reserved word pieces to mark the beginning and the
end of each event argument mention in the sentence,
as in the following example: <#> BREAKING </#>
! 5 Explosions heard near Bataclan theater <#>
fusillade </#> <#> < GPE > Paris </GPE> </#>
<#> <GPE> France </GPE> Shooting</#>.
Event Metadata. Additionally, we concatenate the
augmented tweet text with the event title and type
found in the topic description (e.g., bombing and 2015
Paris attacks).
The TREC-IS dataset has a number of emergency
events covering different types: earthquakes, tropical
storms (e.g., hurricanes), mass violence (e.g., shoot-
ings, bombings), public health emergencies (e.g., epi-
demics), etc. Each incident is accompanied by a brief
topic statement that contains the event title (e.g., 2015
Paris attacks), event type (e.g., bombing), and a narra-
tive or description of the event. The provided dataset
consisted of a total of 73,499 tweets that covered 75
topics. We did not perform any pre-processing on the
data. In our internal experimental setup, we produced
a data split for simulating the official TREC-IS split
that contains unknown event types in the test set
. In
We used the model provided by spaCy v3.0+
bal and Montani, 2017).
The official test set is not available.
KDIR 2022 - 14th International Conference on Knowledge Discovery and Information Retrieval
Table 1: Detailed results for all our proposed models in comparison with a Simple model, which is the RoBERTa model with
[ SentRep and TokRep ] +/- Transformer layers that uses no enhancement/augmentation.
Approach nDCG Info-Type Priority
@100 F1(Act) F1(All) Accuracy F1(Act) F1(All) R(Act) R(All)
Baselines & other models
BERT SentRep (Wang et al., 2021) 0.4467 0.0458 0.1202 0.7296 0.2711 0.1750 0.2440 0.1699
RoBERTa SentRep 0.4256 0.0439 0.1366 0.3644 0.1100 0.1826 0.0000 0.0000
RoBERTa TokRep 0.4777 0.0462 0.1315 0.6411 0.2247 0.2645 0.1480 0.1494
Our models
RoBERTa SentRep + TokRep
Simple 0.5015 0.0942 0.1726 0.8804 0.2711 0.2887 0.2929 0.2186
Ent 0.4967 0.0959 0.1846 0.8824 0.3006 0.3110 0.2598 0.2098
# 0.4873 0.0860 0.1773 0.8817 0.2638 0.2786 0.2504 0.1880
EvtNameType + # 0.4902 0.0579 0.1742 0.8766 0.2456 0.2719 0.2117 0.2089
Ent + # 0.4995 0.0484 0.1638 0.8820 0.2510 0.2751 0.2476 0.1832
Ent+EvtNameType + # 0.4917 0.0083 0.0993 0.8795 0.2320 0.2755 0.1364 0.1702
RoBERTa SentRep + TokRep + 1×Transformer
Simple 0.4831 0.0446 0.1085 0.7260 0.3117 0.3326 0.1512 0.2024
Ent 0.4761 0.1625 0.2385 0.8786 0.3115 0.3211 0.3647 0.2423
# 0.4825 0.1813 0.2291 0.8773 0.3100 0.3249 0.2349 0.1964
EvtNameType + # 0.5414 0.1772 0.2534 0.8829 0.2859 0.3066 0.3427 0.2150
Ent + # 0.4803 0.0704 0.1428 0.8770 0.2799 0.2923 0.2931 0.1904
Ent+EvtNameType+# 0.5052 0.1548 0.2308 0.8824 0.2982 0.3170 0.2764 0.2175
RoBERTa SentRep + TokRep + 2×Transformer
Simple 0.4835 0.1419 0.2139 0.8755 0.3059 0.3228 0.3006 0.2368
Ent 0.4862 0.2058 0.2483 0.8761 0.2977 0.3122 0.3889 0.2413
# 0.4737 0.1555 0.2221 0.8773 0.3038 0.3229 0.2236 0.2042
EvtNameType + # 0.5060 0.1793 0.2552 0.8840 0.3171 0.3296 0.3490 0.2772
Ent + # 0.4750 0.0675 0.1872 0.8818 0.2450 0.2740 0.1327 0.1522
Ent+EvtNameType+# 0.4946 0.1577 0.2449 0.8830 0.3456 0.3290 0.2513 0.1924
RoBERTa SentRep + TokRep + 3×Transformer
Simple 0.4690 0.2102 0.2428 0.8768 0.2670 0.3064 0.1423 0.1779
Ent 0.4779 0.1415 0.2419 0.8768 0.2746 0.3090 0.3073 0.2205
# 0.4892 0.1899 0.2345 0.8777 0.3252 0.3317 0.2670 0.2147
EvtNameType + # 0.5145 0.1759 0.2437 0.8842 0.2567 0.2997 0.3556 0.2416
Ent + # 0.4635 0.1143 0.1647 0.8809 0.2492 0.2801 0.3291 0.2583
Ent + EvtNameType+ # 0.4973 0.1743 0.2529 0.8813 0.2954 0.3209 0.2594 0.2345
Figure 3: The distribution of the performance scores for six different types of input augmentations.
Adapting Transformers for Detecting Emergency Events on Social Media
Figure 4: The distribution of the performance scores for RoBERTa+n×Transformer.
Figure 5: The explorations of the attention behavior for RoBERTa+1×Transformer hashtags and event metadata.
order to ensure this, for the training set, we selected
the event types from 0 to 64 (60,577 tweets), and from
65 to 75 (12,923 tweets), for the test set
. Thus, in this
manner, no events are overlapped in the train and test
4.1 Evaluation
To evaluate the performance of such systems, the
following two groups of metrics were proposed by
TREC: for information type, its overall F1(All) score,
macro-averaged across all types and micro-averaged
More details about the data can be found at http://dcs.
richardm/TREC IS/2020/data.html.
across events, and its F1(Act) score among the ac-
tionable types. For prioritization, its overall prior-
itization error is considered, micro-averaged across
events, F1(Act), and macro-averaged across all types,
F1 (All), and priority scores correlational perfor-
mance, R(All) and R(Act). We experimented with
four models based on RoBERTa + n× Transformer
with n {0, 1, 2, 3} (when n = 0, the token represen-
tations are not used), and six different types of in-
put augmentations: Simple (no augmentations), Ent
(text with marked entities), # (text with marked and
pre-processed hashtags), Ent+# (the previous two
together), EvtNameType+# (hashtags and the event
type and title), and Ent+EvtNameType+#.
KDIR 2022 - 14th International Conference on Knowledge Discovery and Information Retrieval
4.2 Input Augmentations
Figure 3 shows the distribution of the performance
scores for the six different types. For information type
classification (Info-Type), we observe that, while the
highest F1(Act) and F1(All) are obtained when hash-
tags and entities are marked separately, the lowest
scores are obtained when they are combined. We also
notice that augmenting the tweets with the event title
and type, besides hashtags, outperforms the models
that use entities or no augmentations. However, when
the augmentations are performed altogether, the dis-
tribution of the scores is negatively skewed, with the
majority of the models underperforming. The results
without any augmentation tend to vary the most, al-
though the median value remains stable. When de-
tecting the priority, we observe the same tendencies
regarding the entities, while the events and hashtags
generally perform the best. When including only
entities, the R(Act) scores outperform considerably
the other types, while when adding the pre-processed
hashtags and the event titles and types, the R(All) sur-
passes the others.
4.3 Transformer Adapters
Figure 4 shows the distribution of the performance
scores for RoBERTa+n×Transformer. For the pre-
diction of the information type, we notice that F1(All)
and F1(Act) are generally lower when no additional
Transformer layer is used, and increase proportionally
with the number of Transformer layers. For priority,
however, the highest results were brought by adding
just one Transformer, while generally overfitting with
more than one.
For further understanding of the impact of the
Transformer adapters and the input enhancements,
we visualize the last three attention matrices for two
selected layers, the eleventh layer of RoBERTa and
the first Transformer adapter. Based on the Figure
5 above we observe that there is a high attention set
along the diagonals and on an informative tokens such
as BREAKING!! and the event metadata (bombing
and 2015 Paris attacks). For the Transformer layer,
the attention finds correlations between Paris and the-
ater, between hashtags markers < # > and other in-
formative tokens such as France or shooting. Such a
pattern could indicate that the additional Transformer
layers are able to identify correlations between fac-
tual impact factors to detecting emergency events on
social media.
4.4 SentRep versus TokRep
Finally, in Table 1, we compare our methods with a
recent work (Wang et al., 2021) that consists in a mul-
titask BERT with a SentRep, with a regression task
priority and a classification task for information type.
We also compare two base models (with SentRep),
for analyzing the importance of adding TokRep. We
can observe that, generally, the classification of in-
formation type mostly benefits the TokRep and Sen-
tRep together with more than two additional Trans-
former layers applied to them, obtaining F1 scores
higher than 0.20. While we notice that SentRep, for
a regression task (Wang et al., 2021), can be enough
for predicting the priority, we agree more towards the
fact that the most important and impactful factor is the
TokRep, that gain a considerable increase when com-
pared only with SentRep.
Finally, our best results are revealed in Table 1 in
comparison with the state-of-the-art models proposed
by (Wang et al., 2021) and our baseline without any
Our experiments showed that considering the token
sequence encoded by additional adapted Transformer
layers augmented with either entities or event meta-
data could bring promising improvements in detecting
the criticality of events in social media posts. Further
work will be focused on performing different ablation
studies (e.g., number and size of the attention heads)
and error analysis. This work could open new ven-
tures toward more effective emergency and actionable
event detection.
Boros, E., Hamdi, A., Pontes, E. L., Cabrera-Diego, L.-A.,
Moreno, J. G., Sidere, N., and Doucet, A. (2020). Al-
leviating digitization errors in named entity recogni-
tion for historical documents. In Proceedings of the
24th Conference on Computational Natural Language
Learning, pages 431–441.
Boros, E., Moreno, J. G., and Doucet, A. (2021). Event de-
tection with entity markers. In Hiemstra, D., Moens,
M.-F., Mothe, J., Perego, R., Potthast, M., and Sebas-
tiani, F., editors, Advances in Information Retrieval,
pages 233–240, Cham. Springer International Pub-
Buntain, C., McCreadie, R., and Soboroff, I. (2020). In-
cident streams 2020: Trecis in the time of covid-19.
Adapting Transformers for Detecting Emergency Events on Social Media
18th International Conference on Information Sys-
tems for Crisis Response and Management.
Choi, W.-G., Jo, S.-H., and Lee, K.-S. (2018). Cbnu at trec
2018 incident streams track. In TREC.
Chy, A. N., Siddiqua, U. A., and Aono, M. (2018). Neu-
ral networks and support vector machine based ap-
proach for classifying tweets by information types at
trec 2018 incident streams task. In TREC.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K.
(2019). BERT: Pre-training of deep bidirectional
transformers for language understanding. In 2019
Conference of the North American Chapter of the
Association for Computational Linguistics: Human
Language Technologies (NAACL-HLT 2019), pages
4171–4186, Minneapolis, Minnesota. Association for
Computational Linguistics.
Dusart, A., Hubert, G., and Pinel-Sauvagnat, K. (2019). Irit
at trec 2019: Incident streams and complex answer re-
trieval tracks. In Text REtrieval Conference. National
Institute of standards and Technology (NIST).
ıa-Cumbreras, M.
A., D
ıaz-Galiano, M. C., Garc
Vega, M., and Jim
enez-Zafra, S. M. (2018). Sinai at
trec 2018: Experiments in incident streams. Weather,
Honnibal, M. and Montani, I. (2017). spacy 2: Natural lan-
guage understanding with bloom embeddings, convo-
lutional neural networks and incremental parsing. Un-
published software application.
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D.,
Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov,
V. (2019). Roberta: A robustly optimized bert pre-
training approach. arXiv preprint arXiv:1907.11692.
McCreadie, R., Buntain, C., and Soboroff, I. (2019). Trec
incident streams: Finding actionable information on
social media. Proceedings of the 16th ISCRAM Con-
McCreadie, R., Buntain, C., and Soboroff, I. (2020). In-
cident streams 2019: Actionable insights and how to
find them. Proceedings of the International ISCRAM
Mishra, A. and Pal, S. (2019). Iit bhu at trec 2019 incident
streams track. In TREC.
Miyazaki, T., Makino, K., Takei, Y., Okamoto, H., and
Goto, J. (2018). Nhk strl at trec 2018 incident streams
track. In TREC.
Miyazaki, T., Makino, K., Takei, Y., Okamoto, H., and
Goto, J. (2019). Label embedding using hierarchical
structure of labels for twitter classification. In Pro-
ceedings of the 2019 Conference on Empirical Meth-
ods in Natural Language Processing and the 9th Inter-
national Joint Conference on Natural Language Pro-
cessing (EMNLP-IJCNLP), pages 6317–6322.
Moreno, J. G., Boros, E., and Doucet, A. (2020). Tlr at the
ntcir-15 finnum-2 task: Improving text classifiers for
numeral attachment in financial social data. In Pro-
ceedings of the 15th NTCIR Conference on Evalua-
tion of Information Access Technologies, Tokyo Japan,
pages 8–11.
Moreno, J. G., Doucet, A., and Grau, B. (2021). Rela-
tion classification via relation validation. In Proceed-
ings of the 6th Workshop on Semantic Deep Learning
(SemDeep-6), pages 20–27.
Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark,
C., Lee, K., and Zettlemoyer, L. (2018). Deep con-
textualized word representations. In Proceedings of
the 2018 Conference of the North American Chapter
of the Association for Computational Linguistics: Hu-
man Language Technologies, Volume 1 (Long Papers),
pages 2227–2237, New Orleans, Louisiana. Associa-
tion for Computational Linguistics.
Pfeiffer, J., Vuli
c, I., Gurevych, I., and Ruder, S. (2020).
MAD-X: An Adapter-Based Framework for Multi-
Task Cross-Lingual Transfer. In Proceedings of the
2020 Conference on Empirical Methods in Natural
Language Processing (EMNLP), pages 7654–7673,
Online. Association for Computational Linguistics.
Soares, L. B., FitzGerald, N., Ling, J., and Kwiatkowski,
T. (2019). Matching the blanks: Distributional
similarity for relation learning. arXiv preprint
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones,
L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I.
(2017). Attention is all you need. In Advances in
neural information processing systems, pages 5998–
Wang, C. and Lillis, D. (2021). Ucd-cs at trec 2021 incident
streams track. arXiv preprint arXiv:2112.03737.
Wang, C., Nulty, P., and Lillis, D. (2021). Transformer-
based multi-task learning for disaster tweet categori-
sation. arXiv preprint arXiv:2110.08010.
KDIR 2022 - 14th International Conference on Knowledge Discovery and Information Retrieval