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## Punctuation restoration from read text
Restore punctuation marks from ASR outputs.
## Motivation
Speech transcripts generated by Automatic Speech Recognition (ASR)
systems typically do not contain any punctuation or capitalization. In
longer stretches of automatically recognized speech, the lack of
punctuation affects the general clarity of the output text \[1\]. The
primary purpose of punctuation (PR) and capitalization restoration (CR)
as a distinct natural language processing (NLP) task is to improve the
legibility of ASR-generated text, and possibly other types of texts
without punctuation. Aside from their intrinsic value, PR and CR may
improve the performance of other NLP aspects such as Named Entity
Recognition (NER), part-of-speech (POS) and semantic parsing or spoken
dialog segmentation \[2, 3\]. As useful as it seems, It is hard to
systematically evaluate PR on transcripts of conversational language;
mainly because punctuation rules can be ambiguous even for originally
written texts, and the very nature of naturally-occurring spoken
language makes it difficult to identify clear phrase and sentence
boundaries \[4,5\]. Given these requirements and limitations, a PR task
based on a redistributable corpus of read speech was suggested. 1200
texts included in this collection (totaling over 240,000 words) were
selected from two distinct sources: WikiNews and WikiTalks. Punctuation
found in these sources should be approached with some reservation when
used for evaluation: these are original texts and may contain some
user-induced errors and bias. The texts were read out by over a hundred
different speakers. Original texts with punctuation were forced-aligned
with recordings and used as the ideal ASR output. The goal of the task
is to provide a solution for restoring punctuation in the test set
collated for this task. The test set consists of time-aligned ASR
transcriptions of read texts from the two sources. Participants are
encouraged to use both text-based and speech-derived features to
identify punctuation symbols (e.g. multimodal framework \[6\]). In
addition, the train set is accompanied by reference text corpora of
WikiNews and WikiTalks data that can be used in training and fine-tuning
punctuation models.
## Task description
The purpose of this task is to restore punctuation in the ASR
recognition of texts read out loud.
![](media/image1.png){width="6.267716535433071in"
height="7.402777777777778in"}
## Dataset -- WikiPunct
WikiPunct is a crowdsourced text and audio data set of Polish Wikipedia
pages read out loud by Polish lectors. The dataset is divided into two
parts: conversational (WikiTalks) and information (WikiNews). Over a
hundred people were involved in the production of the audio component.
The total length of audio data reaches almost thirty-six hours,
including the test set. Steps were taken to balance the male-to-female
ratio.
WikiPunct has over thirty-two thousand texts and 1200 audio files, one
thousand in the training set and two hundred in the test set. There is a
transcript of automatically recognized speech and force-aligned text for
each text. The details behind the data format and evaluation metrics are
presented below in the respective sections.
**Statistics:**
- **Text:**
- over thirty-two thousand texts; WikiNews ca. 15,000, WikiTalks
> ca. 17,000;
- **Audio:**
- Selection procedure:
- randomly selected WikiNews (80% that is equal 800 entries
> for the training set) with the word count above 150 words
> and smaller than 300 words;
- randomly selected WikiTalks (20%) with word the count above
> 150 words but smaller than 300 words and at least one
> question mark
- Data set split
- Training data: 1000 recordings
- Test data: at 274 recordings
- Speakers:
- Polish male: 51 speakers, 16.7 hours of speech
- Polish female: 54 speakers, 19 hours of speech
**Punctuation for raw text:**
**symbol** **mean** **median** **max** **sum** **included**
---------------------- ------------ ---------- ------------ --------- ----------- --------------
**fullstop** . 12.44 7.0 1129.0 404 378 yes
**comma** , 10.97 5.0 1283.0 356 678 yes
**question_mark** ? 0.83 0.0 130.0 26 879 yes
**exclamation_mark** ! 0.22 0.0 55.0 7 164 yes
**hyphen** \- 2.64 1.0 363.0 81 190 yes
**colon** : 1.49 0.0 202.0 44 995 yes
**ellipsis** \... 0.27 0.0 60.0 8 882 yes
**semicolon** ; 0.13 0.0 51.0 4 270 no
**quote** " 3.64 0.0 346.0 116 874 no
**words** 169.50 89.0 17252.0 5 452 032 \-
The dataset is divided into two parts: conversational (WikiTalks) and
information (WikiNews).
**Part 1. WikiTalks**
Data scraped from Polish Wikipedia Talk pages. Talk pages, also known as
discussion pages, are administration pages with editorial details and
discussions for Wikipedia articles.. Talk pages were scrapped from the
web using a list of article titles shared alongside Wikipedia dump
archives.
Wikipedia Talk pages serve as conversational data. Here, users
communicate with each other by writing comments. Vocabulary and
punctuation errors are expected. This data set covers 20% of the spoken
data.
Example:
- **wikitalks001948:** Cóż za bzdury tu powypisywane! Fra Diavolo
> starał się nie dopuścić do upadku Republiki Partenopejskiej? Kto
> to wymyślił?! Człowiek ten był jednym z najżarliwszych wrogów
> francuskiej okupacji, a za zasługi w wypędzeniu Francuzów został
> mianowany pułkownikiem w królewskiej armii z prawdziwie królewską
> pensją. Bez niego wyzwolenie, nazywać to tak czy też nie,
> północnej części królestwa byłoby dużo trudniejsze, bo dysponował
> siłą kilku tysięcy sprawnych w boju i umiejętnie wziętych w karby
> rzezimieszków. Toteż armia Burbonów nie pokonywała go, jak to się
> twierdzi w artykule, lecz ściśle współpracowała. Redaktorów
> zachęcam do jak najszybszej korekty artykułu, bo aktualnie jest
> obrazą dla ambicji Wikipedii. 91.199.250.17
- **wikitalks008902:** Stare wątki w dyskusji przeniosłem do archiwum.
> Od prawie roku dyskusja w nich nie była kontynuowana. Sławek
> Borewicz
**Part 2. WikiNews**
**Wikinews** is a free-content news wiki and a project of the Wikimedia
Foundation. The site works through collaborative journalism. The data
was scraped directly from wikinews dump archive. The overall text
quality is high, but vocabulary and punctuation errors may occur. This
data set covers 80% of the spoken data.
Example:
- **wikinews222361:** Misja STS-127 promu kosmicznego Endeavour do
> Międzynarodowej Stacji Kosmicznej została przełożona ze względu na
> wyciek wodoru. Podczas procesu napełniania zewnętrznego zbiornika
> paliwem, część ciekłego wodoru przemieniła się w gaz i przedostała
> się do systemu odpowietrzania. System ten jest używany do
> bezpiecznego odprowadzania nadmiaru wodoru z platformy startowej
> 39A do Centrum Lotów Kosmicznych imienia Johna F. Kennedy\'ego.
> Początek misji miał mieć miejsce dzisiaj, o godzinie 13:17. Ze
> względu jednak na awarię, najbliższa możliwa data startu
> wahadłowca to środa 17 czerwca, jednak na ten dzień NASA na
> Przylądku Canaveral zaplanowana wystrzelenie sondy kosmicznej
> Lunar Reconnaissance Orbiter. Misja może być zatem opóźniona do 20
> czerwca, który jest ostatnią możliwą datą startu w tym miesiącu. W
> niedzielę odbędzie się spotkanie specjalistów NASA, na którym
> zostanie ustalona nowa data startu i dalszy plan misji STS-127.
## Data format
Input is a TSV file with two columns:
1. Text ID (to be used when handling forced-aligned transcriptions and WAV files if needed)
2. Input text - in lower-case letter without punctuation marks
The output should have the same number of lines as the input file, in each line
the text with punctuation marks should be given.
### Forced-aligned transcriptions
We use force-aligned transcriptions of the original texts to approximate
ASR output. Files in the *.clntmstmp* format contain forced-alignment of
the original text together with the audio file read out by a group of
volunteers. The files may contain errors resulting from incorrect
reading of the text (skipping fragments, adding words missing from the
original text) and alignment errors resulting from the configuration of
the alignment tool for text and audio files. The configuration targeted
Polish; names from foreign languages may be poorly recognised, with the
word duration equal to zero (start and end timestamps are equal). Data
is given in the following format:
> **(timestamp_start,timestamp_end) word**
>
> **...**
>
> **\</s\>**
where **\</s\>** is a symbol of the end of recognition.
Example:
(990,1200) Rosja
> (1230,1500) zaczyna
>
> (1590,1950) powracać
>
> (1980,2040) do
>
> (2070,2400) praktyk
>
> (2430,2490) z
>
> (2520,2760) czasów
>
> (2820,3090) zimnej
>
> (3180,3180) wojny.
>
> (3960,4290) Rosjanie
>
> (4380,4770) wznowili
>
> (4860,5070) bowiem
>
> (5100,5160) na
>
> (5220,5430) stałe
>
> (5520,5670) loty
>
> (5760,6030) swoich
>
> (6120,6600) bombowców
>
> (6630,7230) strategicznych
>
> (7350,7530) poza
>
> (7590,7890) granice
>
> (8010,8010) kraju.
>
> (8880,9300) Prezydent
>
> (9360,9810) Władimir
>
> (9930,10200) Putin
>
> (10650,10650) wyjaśnił,
>
> (10830,10920) iż
>
> (10980,11130) jest
>
> (11160,11190) to
>
> (11220,11520) odpowiedź
>
> (11550,11640) na
>
> (11670,12120) zagrożenie
>
> (12240,12300) ze
>
> (12330,12570) strony
>
> (12660,12870) innych
>
> (13140,13140) państw.
>
> \</s\>
## Evaluation procedure
Baseline results will be provided in final evaluation.
### Punctuation
During the task the following punctuation marks will be evaluated:
**Punctuation mark** **symbol**
------------------------ ------------
fullstop .
comma ,
question mark ?
exclamation mark !
hyphen \-
colon :
ellipsis \...
blank (no punctuation)
### Submission format
Results are to be submitted in a JSON file with the format matching the
input data. Files with results will be tested against the gold standard
annotations kept in the file with the matching text ID in the file name.
#### Example result directory structure
For a given **poleval_text.test.tar.gz** data set with has the following
structure:
+---------------------------+
| **test/** |
| |
| **json/** |
| |
| **wikitalks109264.json** |
| |
| **wikitalks0017548.json** |
| |
| **wikitalks0017518.json** |
| |
| **wikitalks0017499.json** |
| |
| **...** |
| |
| **csv/** |
| |
| **wikitalks109264.csv** |
| |
| **wikitalks0017548.csv** |
| |
| **wikitalks0017518.csv** |
| |
| **wikitalks0017499.csv** |
| |
| **...** |
+---------------------------+
This is the directory structure for **poleval_wav.test.tar.gz**:
+--------------------------------+
| **poleval_final_dataset_wav/** |
| |
| **test/** |
| |
| **wikitalks109264.wav** |
| |
| **wikitalks0017548.wav** |
| |
| **wikitalks0017518.wav** |
| |
| **wikitalks0017499.wav** |
| |
| **...** |
+--------------------------------+
Here is the directory structure for **poleval_fa.test.tar.gz**:
+--------------------------------+
| **poleval_final_dataset/** |
| |
| **test/** |
| |
| **wikitalks109264.clntmstmp** |
| |
| **wikitalks0017548.clntmstmp** |
| |
| **wikitalks0017518.clntmstmp** |
| |
| **wikitalks0017499.clntmstmp** |
| |
| **...** |
+--------------------------------+
The correct submission format should be:
+---------------------------+
| **system_response/** |
| |
| **wikitalks109264.json** |
| |
| **wikitalks0017548.json** |
| |
| **wikitalks0017518.json** |
| |
| **wikitalks0017499.json** |
| |
| **...** |
+---------------------------+
#### Schema validation
Use *jsonschema* to run a sanity check against the file with the
results:
[[https://pypi.org/project/jsonschema/]{.ul}](https://pypi.org/project/jsonschema/)
------------------------------------------------
**\$ jsonschema -i result.json result.schema**
------------------------------------------------
For multiple files:
-----------------------------------------------------------------------
**\$ ls result/\*.json \| xargs -I{} jsonschema -i {} result.schema**
-----------------------------------------------------------------------
### Evaluation Script
Evaluation script will be provided on the task's page.
------------------------------------------------------------
**\$ python3 evaluate.py gold_directory system_directory**
------------------------------------------------------------
### Metrics
Final results are evaluated in terms of precision, recall, and F1 scores
for predicting each punctuation mark separately. Submissions are
compared with respect to the weighted average of F1 scores for each
punctuation sign.
##### Per-document score:
$Precision\ = \ \frac{\text{TP}}{TP\ + \ FP}$$;\ Recall\ = \ \frac{\text{TP}}{TP\ + \ FN}$${;\ F}_{1}\ = \ 2\ *\ \frac{Precision\ *\ Recall}{Precision\ + \ Recall}$
##### Global score per punctuation sign *p*:
$P_{p}\ = avg_{\text{micro}}Precision(p)\ = \frac{\text{TP}}{TP + FP}\text{\ \ \ \ \ \ \ \ }R_{p} = avg_{\text{micro}}Recall(p)\ = \frac{\text{TP}}{TP + FN}\ $
Final scoring metric calculated as weighted average of global scores per
$\frac{1}{N}support(p)\ *\ avg_{\text{micro}}F_{1}(p)$
We would like to invite participants to discussion about evaluation
metrics, taking into account such factors as:
- ASR and Forced-Alignment errors,
- inconsistencies among annotators,
- impact of only slight displacement of punctuation,
- assigning different weights to different types of errors.
### Downloads
Data can be downloaded from Google Drive. Below is a list of file names
along with a description of what they contain.
- poleval_fa.train.tar.gz - archive contain forced-alignment of the
> original text together with the audio file
- poleval_text.train.tar.gz - archive contain original text in
> provided JSON format and CSV corresponding to audio files
- poleval_text.rest.tar.gz - archive contain original text in provided
> JSON format and CSV for which no audio files were provided
- poleval_wav.train.tar.gz - archive contain audio files
### References
1. Yi, J., Tao, J., Bai, Y., Tian, Z., & Fan, C. (2020). Adversarial
> transfer learning for punctuation restoration. *arXiv preprint
> arXiv:2004.00248*.
2. Nguyen, Thai Binh, et al. \"Improving Vietnamese Named Entity
> Recognition from Speech Using Word Capitalization and Punctuation
> Recovery Models.\" *Proc. Interspeech 2020* (2020): 4263-4267.
3. Hlubík, Pavel, et al. \"Inserting Punctuation to ASR Output in a
> Real-Time Production Environment.\" *International Conference on
> Text, Speech, and Dialogue*. Springer, Cham, 2020.
4. Sirts, Kairit, and Kairit Peekman. \"Evaluating Sentence
> Segmentation and Word Tokenization Systems on Estonian Web
> Texts.\" *Human Language Technologies--The Baltic Perspective:
> Proceedings of the Ninth International Conference Baltic HLT
> 2020*. Vol. 328. IOS Press, 2020.
5. Wang, Xueyujie. \"Analysis of Sentence Boundary of the Host\'s
> Spoken Language Based on Semantic Orientation Pointwise Mutual
> Information Algorithm.\" *2020 12th International Conference on
> Measuring Technology and Mechatronics Automation (ICMTMA)*.
> IEEE, 2020.
6. Sunkara, Monica, et al. \"Multimodal Semi-supervised Learning
> Framework for Punctuation Prediction in Conversational Speech.\"
> *arXiv preprint arXiv:2008.00702* (2020).
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