Update README.md

README.md

@@ -56,28 +56,70 @@ German
 
 ### Data Instances
 
-[More Information Needed]
+On sentence level, each instance consists of a unique identifier consisting of <dialog-id_turn-id_sentence-id>, the sentence (raw), the label, and the subsplit. 
+
+```
+"178_0_1";"wie kriege ich die Dichtung raus?";"P";"Batch 3"
+```
+
+On token level, each instance consists of a unique identifier, a list of tokens containing the whole dialog, the list of labels (bio-tagged entities), and the subsplit.
+```
+"178_0";"['Hi', 'wie', 'kriege', 'ich', 'die', 'Dichtung', 'raus', '?', 'in', 'der', 'Schublade', 'gibt', 'es', 'einen', 'Dichtungszieher']";"['O', 'O', 'O', 'O', 'O', 'B-PRE', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'B-PE']";"Batch 3"
+```
 
 ### Data Fields
 
-[More Information Needed]
+Sentence level: 
+
+* id: the identifier
+* sentence: the respective sentence
+* label: the label (_P_ for Problem, _C_ for Cause, _S_ for solution, and _O_ for Other)
+* subsplit: the respective subsplit of the data (see below)
+
+Token level: 
+
+* id: the identifier
+* tokens: a list of tokens (i.e., the tokenized dialogue) 
+* entities: the named entity in a BIO scheme (_B-X_, _I-X_, or O). 
+* subsplit: the respective subsplit of the data (see below)
+
 
 ### Data Splits
 
-[More Information Needed]
+The dataset is split into train and test splits, but contains further subsplits (subsplit column). Note, that the splits are collected at different times with some turnaround in the workforce. Hence, later data (especially the data from batch 2) contains more turns (due to increased search for a cause) as more inexperienced workers who newly joined were employed in the factory. 
+
+Train:
+* Batch 1 industrie: data collected in October 2020 from workers in the industry 4.0 assembly line
+* Batch 1 zerspanung: data collected in October 2020 from workers in the machining assembly line
+* Batch 2: data collected in-between October 2021-June 2022 from all workers
+
+Test:
+* Batch 3: data collected in July 2022 together with the system usability study run
+
+Sentence level statistics: 
+
+| Batch  | Dialogues | Turns | Sentences |   
+|---|---|---|---|
+| 1  |  80 | 188  | 553  |   
+| 2 |  97 | 309  | 432  |   
+| 3  |  24 | 36  | 42  |   
+|---|---|---|---|
+| Overall | 201 | 523 | 1,027 |   
+
+Token level statistics: 
+[to follow]
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-[More Information Needed]
+This dataset provides task-oriented dialogues that solve a very domain specific problem.  
 
 ### Source Data
 
 #### Initial Data Collection and Normalization
 
-The data was generated by workers at the [CiP](https://www.prozesslernfabrik.de/)
-The data was collected in three rounds. First, 
+The data was generated by workers at the [CiP](https://www.prozesslernfabrik.de/). The data was collected in three rounds (October 2020, October 2021-June 2022, July 2022). As the dialogues occurred during their daily work, one distinct property of the dataset is that all dialogues are very informal, contain abbreviations, and filler words such as 'eh'. For a detailed description please see the [paper](https://arxiv.org/abs/2208.07846).
 
 #### Who are the source language producers?
 
@@ -87,20 +129,14 @@ German factory workers working at the [CiP](https://www.prozesslernfabrik.de/)
 
 #### Annotation process
 
-##### Token level
-Token level annotation was done by researchers who are responsible for supervising and teaching workers at the CiP. The data was first split into three parts, each annotated by one researcher. Next, each researcher cross-examined the other researchers' annotations. If there were disagreements, all three researchers discussed the final label.
-
-##### Sentence level
-Sentence level annotations were collected from the factory workers who also generated the dialogues. For details about the data collection, please see the [TexPrax demo paper](https://arxiv.org/abs/2208.07846).
+**Token level.** Token level annotation was done by researchers who are responsible for supervising and teaching workers at the CiP. The data was first split into three parts, each annotated by one researcher. Next, each researcher cross-examined the other researchers' annotations. If there were disagreements, all three researchers discussed the final label.
 
+**Sentence level.** Sentence level annotations were collected from the factory workers who also generated the dialogues. For details about the data collection, please see the [TexPrax demo paper](https://arxiv.org/abs/2208.07846).
 
 #### Who are the annotators?
 
-##### Token level
-Researchers working at the CiP.
-
-##### Sentence level
-The factory workers themselves.
+Token level: Researchers working at the CiP.
+Sentence level: The factory workers themselves.
 
 ### Personal and Sensitive Information
 
@@ -110,11 +146,11 @@ This dataset is fully anonymized. All occurrences of names have been manually ch
 
 ### Social Impact of Dataset
 
-[More Information Needed]
+Informal language especially used in short messages, however, seldom considered in existing NLP datasets. This dataset could serve as an interesting evaluation task for transferring language models to low-resource, but highly specific domains. Moreover, we note that despite all abbreviations, typos, and local dialects used in the messages, all workers were able to understand the questions as well as replies. This should be a standard future NLP models should be able to uphold. 
 
 ### Discussion of Biases
 
-[More Information Needed]
+The dialogues are very much on a professional level. The workers were informed (and gave their consent) in advance that their messages are being recorded and processed, which may have influenced them to hold only professional conversations, hence, all dialogues concern inanimate objects (i.e., machines). 
 
 ### Other Known Limitations