SlowGuess/NAACL_2022_OCR huggingface dataset:
Ellery Smith, Martin Braschler, Andreas Weiler, and Thomas Haberthuer. 2019. Syntax-based skill extractor for job advertisements. In 2019 6th Swiss Conference on Data Science (SDS), pages 80-81. IEEE.
Ellery Smith, Andreas Weiler, and Martin Braschler. 2021. Skill extraction for domain-specific text retrieval in a job-matching platform. In International Conference of the Cross-Language Evaluation Forum for European Languages, pages 116-128. Springer.
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Damian A Tamburri, Willem-Jan Van Den Heuvel, and Martin Garriga. 2020. Dataops for societal intelligence: a data pipeline for labor market skills extraction and matching. In 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI), pages 391-394. IEEE.
Erik F. Tjong Kim Sang. 2002. Introduction to the CoNLL-2002 shared task: Language-independent named entity recognition. In COLING-02: The 6th Conference on Natural Language Learning 2002 (CoNLL-2002).
Dennis Ulmer. 2021. deep-significance: Easy and Better Significance Testing for Deep Neural Networks. https://github.com/Kaleidophon/deep-significance.
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Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumont, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38-45, Online. Association for Computational Linguistics.
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Meng Zhao, Faizan Javed, Ferosh Jacob, and Matt McNair. 2015. SKILL: A system for skill identification
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A Data Statement SKILLSPAN
Following Bender and Friedman (2018), the following outlines the data statement for SKILLSPAN:
A. CURATION RATIONALE: Collection of job postings in the English language for span-level sequence labeling, to study the impact of sequence labeling on the extraction of skill and knowledge components from job postings.
B. LANGUAGE VARIETY: The non-canonical data was collected from the StackOverflow job posting platform, an in-house job posting collection from our national labor agency collaboration partner (which will be elaborated upon acceptance), and web extracted job postings from a large job posting platform. US (en-US) and British (en-GB) English are involved.
C. SPEAKER DEMOGRAPHIC: Gender, age, race-ethnicity, socioeconomic status are unknown.
D. ANNOTATOR DEMOGRAPHIC: Three hired project participants (age range: 25-30), gender: one female and two males, white European and Asian (non-Hispanic). Native language: Danish, Dutch. Socioeconomic status: higher-education students. Female annotator is a professional annotator with a background in Linguistics and the two males with a background in Computer Science.
E. SPEECH SITUATION: Standard American or British English used in job postings. Time frame of the data is between 2012-2021.
F. TEXT CHARACTERISTICS: Sentences are from job postings posted on official job vacancy platforms.
G. RECORDING QUALITY: N/A.
H. OTHER: N/A.
I. PROVENANCE APPENDIX: The job posting data in TECH is from Stackoverflow jobs, and is licensed under the CC BY-SA license. The job posting data from HOUSE is from our collaborators: The Danish Agency for Labour Market and Recruitment (STAR).
B.1 Span Specifications
Legend: Skill, Knowledge, “·” indicates an example sentence.
1. A skill starts with a VERB, otherwise (ADJECTIVE) + NOUN
1.1 Modal verbs are not tagged:
• Can [put personal touch on the menu]SKILL.
• Will [train new staff]SKILL.
2. Split up phrases with prepositions and/or conjunctions
2.1 Unless the conjunction coordinates two nouns functioning as one argument:
• [Coordinate parties and conferences]SKILL.
2.2 Do not tag skills with anaphoric pronouns, only tag preceding skill:
• [Prioritizing tasks]SKILL and identifying those that are most important.
2.3 Split nouns and adjectives that are coordinated if they do not have a verb attached:
• Be [inquisitive]SKILL and [proactive]SKILL.
• Prior in-house experience with [media]KNOWLEDGE, [publishing]KNOWLEDGE or [internet companies]KNOWLEDGE.
2.4 If there is a listing of skill tags and they lead up to different subtasks, we split them:
• [keep up the high level of quality in our team]SKILL through [reviews]SKILL, [pairing]SKILL and [mentoring]SKILL.
3. If there is relevant information appended after irrelevant information (e.g., info specific to a company) we try to make the skill as short as possible:
• [providing the best solution]SKILL for Siemens Games in a very structured]SKILL and [analytic]SKILL manner.
4. Note also the words skills and knowledge can be included in the span of the component if leaving it out makes it nonsensical:
• [personal skills]SKILL → just [personal] would make it nonsensical.
5. Parentheses after a skill tag are included if they elaborate the component before them or if they are an abbreviation of the component.
6. Inclusion of adverbials in components. Adverbials are included if it concerns the manner of doing something. All others are excluded:
· like to [solve technical challenges independently]SKILL.
· [communicates openly]SKILL.
· [striving for the best]SKILL in all that they do.
· [Deliver first class customer service]SKILL to our guests.
· [Making the right decisions]SKILL early in the process.
7. Attitudes as skills. We annotate attitudes as a skill:
· a [can-do-approach]SKILL → we leave out articles from the attitude.
8. Attitudes are not tagged if they contain skill/knowledge components—then only the span of the skill is tagged.
· like to [solve technical challenges independently]SKILL.
· Passion for [automation]KNOWLEDGE.
· enjoy [working in a team]SKILL.
9. Miscellaneous:
9.1 Do not tag ironic skills (e.g., lazy).
9.2 Avoid nesting of skills, annotate it as one span.
9.3 We annotate all skills that are part of sections such as “requirements”, “good-to-haves”, “great-to-knows”, “optionals”, “after this x months of training you’ll be able to...”, “At the job you’re going to...”.
9.4 When there is a general standard that can be added to the skill, we add these:
· [Process payments according to the [...] standards]SKILL.
B.2 Knowledge Specifications
1. Rule-of-thumb: knowledge is something that one possesses, and cannot (usually) physically execute:
· [Python]KNOWLEDGE (programming language).
· [Business]KNOWLEDGE.
· [Relational Databases]KNOWLEDGE.
2. If there is a component between parentheses that belongs to the knowledge component, we add it:
· [(non-) relational databases]KNOWLEDGE.
· [Driver License (UK/EU)]KNOWLEDGE.
3. Licenses and certifications: We add the additional words “certificate”, “card”, “license”, et cetera. to the knowledge component.
4. If the knowledge component looks like a skill, but the preceding verb is vague and empty (e.g.,follow, use, comply with, work with) → only tag the knowledge component:
· Comply with [Food Code of Practice]KNOWLEDGE.
· Work with [AWS infrastructure]KNOWLEDGE.
5. We annotate only specified knowledge components:
· [MongoDB]KNOWLEDGE or other [NoSQL database]KNOWLEDGE.
· [JEST]KNOWLEDGE or other test libraries. → “other test libraries” is under-specified.
6. Knowledge components can be nested in skill components.
· [Design, execution and analysis of [phosphoproteomics]KNOWLEDGE experiments]SKILL.
7. If all components coordinate/share one knowledge tag, we annotate it as one:
· [application, data and infrastructure architecture]KNOWLEDGE. → The knowledge tags coordinate to “architecture”.
· [chemical/biochemical engineering]KNOWLEDGE.
8. If there is a listing of knowledge tags, we annotate all knowledge tags separately:
· [Bachelor Degree]KNOWLEDGE in [Mathematics]KNOWLEDGE, [Computer Science]KNOWLEDGE, or [Engineering]KNOWLEDGE.
B.3 Other Specifications
1. Rule-of-thumb: If in doubt, annotate it as a skill.
2. We are preferring skills over knowledge components.
3. We prioritize skills over attitudes; if there is a skill within the attitude, only tag the skill:
• Passionate-around [solving business problems]SKILL through
[innovation & engineering practices]KNOWLEDGE.
4. Skill or knowledge components in the top headlines of the JP are not tagged (e.g., title of a JP). If it is a sub-headline or in the rest of the posting, tag it.
5. We try to keep the skill/knowledge components as short as possible (i.e., exclude information at the end if it makes it too specific for the job).
6. We do not include “fluff” and “triggers” (i.e., words that indicate a skill or knowledge component will follow: “advanced knowledge of [...]KNOWLEDGE”) around the components, including degree. This goes for both before and after:
• Working proficiency in [developmental toolsets]KNOWLEDGE.
• Advanced knowledge of [application data and architecture infrastructure]KNOWLEDGE disciplines.
• [Manual handling]SKILL tasks.
• [CI/CD]KNOWLEDGE experience.
• You master [English]KNOWLEDGE on-level-C1.
• Proficient in [Python]KNOWLEDGE and [English]KNOWLEDGE.
• Fluent in spoken and written [English]KNOWLEDGE.
7. Pay attention to expressions such as “participation in...”, “contributing”, and “transfer (knowledge)”. These are usually not considered skills.
• Contribute to the enjoyable and collaborative work environment.
• Participation in the Department's regular research activities.
• Desire to be part of something meaningful and innovative.
8. Skills and Knowledge components that are found in not-so-straightforward places (e.g., project descriptions) are annotated as well, if they relate to the position.
9. In the pattern of “skill” followed by some elaboration, see if it can be annotated with a skill and a knowledge tag:
• [Ensure food storage and preparation areas are maintained]SKILL according to
[Health & Safety and Audit standards]KNOWLEDGE.
10. Occupations and positions in companies/academia should be excluded.
11. If there's a knowledge/skill component in the position, we exclude it as well.
· Experienced Java Engineer. → completely untagged.
12. Only annotate the skills that are related to the position.
12.1. This includes skills that are specific for the position as well (e.g., skills of a ruminants professor versus math professor).
12.2 Also skills that the person for the position is expected to do in the future.
12.3 This does not include skills, knowledge or attitudes describing only the company, the group you will join in the department, and so on. Only annotate if it is specified or implied that the employee should possess the skill as well.
13. We annotate industries and fields (that the employee will be working in) as knowledge components.
C Type of Skills Annotated
In both Table 8 and Table 9, we show the top-10 skill and knowledge components that have been annotated. We split the top-10 among the data splits (i.e., train, development, and test set), and also between source splits (i.e., BIG, HOUSE, TECH).
Parameter	Value	Range
Optimizer	AdamW
β1, β2	0.9, 0.99
Dropout	0.2	0.1, 0.2, 0.3
Epochs	20
Batch Size	32
Learning Rate (LR)	1e-4	1e-3, 1e-4, 1e-5
LR scheduler	Slanted triangular
Weight decay	0.01
Decay factor	0.38	0.35, 0.38, 0.5
Cut fraction	0.2	0.1, 0.2, 0.3
Table 4: Hyperparameters of MACHAMP.
We use the default hyperparameters in MACHAMP (van der Goot et al., 2021) as shown in Table 4. For more details we refer to their paper. For the five random seeds we use 3477689, 4213916, 6828303, 8749520, and 9364029. All experiments with MACHAMP were ran on an NVIDIA® Titan X (Pascal) 12 GB GPU and an Intel® Xeon® Silver 4214 CPU.
E Exact Number of Performance
In Table 5, we show the exact numbers of the plot indicated in Figure 3. In addition, we also show the results of each respective split.
For the STL models, we observe differences in performances over the sources which is particularly pronounced for knowledge components: The TECH source is the easiest to process (and has most SKCs), while SKCs identification performance is the lowest for BIG. This might be due to the broad nature of this source.
In the exact results table (Table 5) we add a  $(\dagger)$  next to the highest span-F1 if the model is truly stochastically dominant  $(\epsilon_{\mathrm{min}} = 0.0)$  over all the other models.  $(^{*})$  denotes that the best model achieved almost stochastic dominance  $(\epsilon_{\mathrm{min}} < 0.5)$  over—at minimum—one other model (e.g., in TEST rows w.r.t COMBINED: MTL-JobBERT  $\succeq$  MTL-JobSpanBERT with  $\epsilon_{\mathrm{min}} = 0.06$ ) and stochastically dominant over the rest.
In Table 6, we report the precision and recall of the models, SKILL and KNOWLEDGE show the precision and recall of the STL models. MULTI shows the precision and recall of the MTL models.
Last, in Table 7, we show the exact numbers of the length of predictions Figure 5. We also add the number of predicted SKILL and KNOWLEDGE Overall, JobBERT and JobSpanBERT predict more skills in general than the other models. This is also the case for knowledge components. We hypothesize that this might be due to the BERT models now being more specialized towards the JP domain and recognizing more SKCs.
F Significance Testing
Recently, the ASO test (Dror et al., 2019) $^{10}$  has been proposed to test statistical significance for deep neural networks over multiple runs. Generally, the ASO test determines whether a stochastic order (Reimers and Gurevych, 2018) exists between two models or algorithms based on their respective sets of evaluation scores. Given the single model scores over multiple random seeds of two algorithms  $\mathcal{A}$  and  $\mathcal{B}$ , the method computes a test-specific value  $(\epsilon_{\mathrm{min}})$  that indicates how far algorithm  $\mathcal{A}$  is from being significantly better than algorithm  $\mathcal{B}$ . When distance  $\epsilon_{\mathrm{min}} = 0.0$ , one can claim that  $\mathcal{A}$  stochastically dominant over  $\mathcal{B}$  with a predefined significance level. When  $\epsilon_{\mathrm{min}} < 0.5$  one can say  $\mathcal{A} \succeq \mathcal{B}$ . On the contrary, when we have  $\epsilon_{\mathrm{min}} = 1.0$ , this means  $\mathcal{B} \succeq \mathcal{A}$ . For  $\epsilon_{\mathrm{min}} = 0.5$ , no order can be determined. We took 0.05 for the predefined significance level. In Figure 7, we show the ASO scores on the development set.
10Implementation of Dror et al. (2019) can be found at https://github.com/Kaleidophon/deep-significance(Ulmer, 2021)
Evaluation →	SKILL	KNOWLEDGE	COMBINED
Src.	↓ Model, Task →	STL	MTL	STL	MTL	STL (*2)	MTL
BIG	BERTbase	59.55±0.97	58.88±1.14	50.68±3.25	51.10±1.67	57.46±1.19	57.00±0.91
SpanBERT	59.78±0.44	60.02±2.15	50.65±2.32	51.79±2.12	57.71±0.53	58.00±2.07
JobBERT	60.60±0.81	59.76±0.60	50.29±1.86	47.59±1.11	58.19±0.49	56.75±0.50
JobSpanBERT	60.16±0.61	59.44±1.11	45.20±2.76	47.69±3.38	56.56±0.49	56.58±0.63
HOUsE	BERTbase	56.83±1.29	55.89±1.90	55.00±1.11	54.05±1.00	56.17±0.92	55.20±1.35
SpanBERT	57.54±1.08	57.30±0.84	52.01±1.72	51.48±1.01	55.55±1.10	55.09±0.74
JobBERT	59.81±1.17	59.97±0.85	54.94±1.15	54.23±2.60	58.02±0.93	57.80±1.50
JobSpanBERT	59.97±1.03	59.62±0.74	55.66±1.51	53.10±1.27	58.37±1.07	57.14±0.56
TECH	BERTbase	59.05±0.71	58.34±0.75	64.08±1.04	63.77±1.18	62.10±0.67	61.65±0.62
SpanBERT	58.39±0.46	58.61±1.14	62.68±0.60	63.40±0.93	61.02±0.35	61.56±0.81
JobBERT	59.81±0.75	59.36±0.90	64.57±0.42	63.15±0.94	62.69±0.40	61.67±0.90
JobSpanBERT	60.09±1.43	59.48±0.61	63.40±1.51	63.23±0.64	62.09±0.85	61.80±0.54
AVERAGE	BERTbase	58.45±0.68	57.67±1.01	60.44±0.58	59.98±0.75	59.35±0.46	58.72±0.48
SpanBERT	58.53±0.33	58.60±0.83	58.89±0.49	59.21±0.78	58.69±0.36	58.88±0.64
JobBERT	60.05±0.70	59.69±0.62	60.66±0.43*	59.15±1.07	60.32±0.39*	59.44±0.81
JobSpanBERT	60.07±0.30†	59.51±0.68	59.47±1.31	59.04±0.65	59.79±0.53	59.29±0.43
TEST	BERTbase	54.34±0.74	54.20±0.68	62.43±0.41	61.66±0.83	58.16±0.47	57.73±0.66
JobBERT	56.11±0.49	55.46±0.75	63.88±0.28*	63.35±0.30	59.73±0.38†	59.18±0.37
JobSpanBERT	56.64±0.83*	56.27±0.55	61.06±0.99	61.87±0.55	58.72±0.69	58.90±0.48
Table 5: Performance of Models. We test the models on skills, KNOWLEDGE, and COMBINED (MTL). We report the span-F1 and standard deviation of runs on five random seeds on the development set (AVERAGE, in gray). Results on the test set are below in the TEST rows (in cyan). STL indicates single-task learning and MTL indicates the multi-task model. Bold numbers indicate best performing model in that experiment. A  $(\dagger)$  means that it is stochastically dominant over all the other models.  $(*)$  denotes almost stochastic dominance  $(\epsilon_{\mathrm{min}} < 0.5)$  over—at minimum—one other model.
Evaluation →	SKILL	KNOWLEDGE	MULTI
Src.	↓ Model	Precision	Recall	Precision	Recall	Precision	Recall
BIG	BERTbase	57.09±1.70	62.27±1.28	43.95±4.17	60.00±1.65	52.63±1.32	62.19±0.87
SpanBERT	58.28±0.59	61.36±0.68	45.80±2.89	56.82±3.39	54.02±1.81	62.63±2.60
JobBERT	57.90±1.25	63.59±0.99	43.45±1.98	59.84±3.44	51.13±0.48	63.74±0.79
JobSpanBERT	58.39±1.03	62.09±1.85	38.55±3.12	54.76±3.18	52.22±0.35	61.75±1.22
HOUsE	BERTbase	55.95±2.46	57.79±0.67	52.84±0.65	57.42±2.76	51.65±1.11	59.28±2.07
SpanBERT	56.70±1.59	58.44±1.16	49.87±2.57	54.49±3.09	52.27±0.64	58.25±1.50
JobBERT	58.16±1.30	61.56±1.53	51.18±2.18	59.37±1.34	53.72±1.57	62.56±1.47
JobSpanBERT	59.04±0.85	60.99±2.58	51.36±2.70	60.84±1.19	53.91±0.77	60.79±0.54
TECH	BERTbase	58.28±1.30	59.89±1.39	60.79±1.89	67.79±1.20	58.19±1.12	65.55±0.75
SpanBERT	58.62±0.32	58.16±0.76	59.43±1.21	66.35±1.18	58.34±0.97	65.17±1.41
JobBERT	58.81±1.38	60.88±1.51	61.38±1.11	68.14±1.36	57.69±0.93	66.25±0.90
JobSpanBERT	59.86±3.07	60.40±0.68	59.78±2.43	67.57±1.97	58.26±0.82	65.82±0.91
AVERAGE	BERTbase	57.11±1.65	59.90±0.95	56.86±1.33	64.54±1.31	55.02±0.85	62.98±0.93
SpanBERT	57.85±0.65	59.23±0.52	55.65±1.09	62.58±1.56	55.61±0.61	62.58±1.25
JobBERT	58.29±1.08	61.94±1.16	56.73±1.41	65.22±1.03	55.03±0.84	64.62±0.77
JobSpanBERT	59.11±1.59	61.12±1.49	55.11±2.41	64.66±1.38	55.64±0.56	63.46±0.69
TEST	BERTbase	56.02±1.50	52.79±1.18	59.09±0.85	66.20±1.69	55.82±1.03	59.79±0.87
JobBERT	55.94±1.19	56.29±0.49	60.03±1.13	68.30±1.46	55.87±0.29	62.89±0.56
JobSpanBERT	57.57±1.24	55.77±1.65	57.83±1.03	64.71±2.10	57.06±0.74	60.89±0.42
Table 6: Precision and Recall of Models. We test the models on skills, knowledge, and multi-task setting. We report the average precision, recall and standard deviation of runs on five random seeds on the development set (AVERAGE). Results on the test set are below in the TEST rows.
Source →	BIG	HOUSE	TECH
↓ Model	SKILLS (#)	KNOWLEDGE (#)	SKILLS (#)	KNOWLEDGE (#)	SKILLS (#)	KNOWLEDGE (#)
ANNOTATIONS	4.16 (634)	2.03 (242)	3.81 (637)	1.91 (350)	3.92 (459)	1.69 (834)
BERTbase	4.42±0.11 (628)	2.17±0.06 (307)	3.89±0.11 (615)	1.98±0.04 (461)	4.43±0.06 (449)	1.75±0.02 (885)
SpanBERT	4.50±0.04 (621)	2.14±0.03 (298)	3.92±0.04 (597)	2.03±0.03 (441)	4.33±0.06 (444)	1.76±0.03 (869)
JobBERT	4.38±0.11 (670)	2.10±0.06 (313)	3.97±0.08 (650)	1.99±0.04 (470)	4.42±0.10 (479)	1.72±0.03 (932)
JobSpanBERT	4.51±0.09 (629)	2.08±0.05 (313)	3.95±0.11 (623)	2.01±0.06 (452)	4.48±0.12 (439)	1.71±0.03 (875)
Longformer	4.45±0.14 (653)	2.22±0.04 (298)	3.90±0.17 (639)	1.97±0.03 (483)	4.40±0.10 (472)	1.80±0.05 (864)
Table 7: Average Length of Predictions of Single Models. We show the average length of the predictions versus the length of our annotated skills and knowledge components on the test set and the total number of predicted skills and knowledge tags in each respective split (#).
Figure 7: Almost Stochastic Order Scores of the Development Set. ASO scores expressed in  $\epsilon_{\mathrm{min}}$ . The significance level  $\alpha = 0.05$  is adjusted accordingly by using the Bonferroni correction (Bonferroni, 1936). Read from row to column: E.g., STL-JobBERT (row) is stochastically dominant over STL-BERT<sub>base</sub> (column) with  $\epsilon_{\mathrm{min}}$  of 0.00.
Skills
Src.	Train	Development	Test
Big	enthusiastic	ambitious	customer service
flexible	proactive	communicator
team player	work independently	flexible
friendly	attention to detail	attention to detail
attention to detail	motivated	ambitious
communicator	reliable	design and refine every touchpoint of the customer journey
passionate	flexible	enable inclusion
communication	willingness to learn	communicate effectively
confident	self-motivated	interpersonal skills
flexible approach	work as part of an established team	proactive
House	communication skills	structured	teaching
motivated	teaching	research
structured	communication skills	communication skills
proactive	project management	outgoing
analytical	drive	flexible
communication	problem solving	energetic
self-driven	communication	responsible
team player	visit customers	enthusiastic
teaching	curious	team player
curious	work independently	communication
TECH	communication skills	hands-on	solving business problems
passionate	communication skills	apply your depth of knowledge and expertise
apply your depth of knowledge and expertise	leadership	partner continuously with your many stakeholders
partner continuously with your many stakeholders	passionate	achieve organizational goals
solving business problems through innovation and engineering practices	open-minded	building an innovative culture
work in large collaborative teams	code reviews	stay focused on common goals
hands-on	independent	work in large collaborative teams
building an innovative culture	software development	design
team player	pioneer new approaches	development
develop	analytical skills	communicate
Table 8: Most Frequent Skills in the Data. Top-10 skill components in our data in terms of frequency.
Knowledge
Src.	Train	Development	Test
Big	english	full uk driving licence	strategic planning
driving license	sap energy assessments	english
excel	right to work in the uk	cscs card
cscs card	sen	pms
maths	acca/aca	reservation systems
ppc	professional kitchen	keynote
service design	cra calculations	illustrator
uk/emea policies	email marketing	aba
bachelor's degree	qualitative and quantitative social research methods	sen
computer science	care setting	full driving license
HOUSE	english	english	english
engineering	supply chain	danish
computer science	project management	business
product management	powders	java
python	machine learning	marketing
finance	phd degree	plm
project management	muscle models with learning and adaptation	production
agile	walking robots	supply chain
danish	model rules	economics
javascript	capacity development	excel
TECH	javascript	java	java
python	javascript	python
java	aws	.net
agile	docker	financial services
financial services	node.js	c#
node.js	typescript	javascript
english	react	cloud
kubernetes	linux	english
cloud	amazon-web-services	reactjs
docker	devops	automation
Table 9: Most Frequent Knowledge in the Data. Top-10 knowledge components in our data in terms of frequency.