SlowGuess/NAACL_2021_OCR huggingface dataset:
Table 8: Performance evaluation of the QGwithAns, QGwithEnt, and DescribeEnt operator for different models. The best performance is in bold. We adopt the Google-T5 and the GPT2-Medium in our model MQA-QG.
Comparative Property	#	Question Template	Answer
born, birthdate	1	Who was born first, e1 or e2?	e1 / e2
located, location	2	Are e1 and e2 located in the same place?	Yes / No
3	Which one is located in a1, e1 or e2?	e1
4	Which one is located in a2, e1 or e2?	e2
5	Are both e1 and e2 located in a1?	Yes / No
nationality, nation, country	6	Are e1 and e2 of the same nationality?	Yes / No
7	Which person is from a1, e1 or e2?	e1
8	Which person is from a2, e1 or e2?	e2
live, live place, hometown	9	Are e1 and e2 living in the same place?	Yes / No
10	Which person lives in a1, e1 or e2?	e1
11	Which person lives in a2, e1 or e2?	e2
Table 9: The comparative properties and their corresponding question templates used in the CompBlend operator.  $a1 / a2$  denotes the answer for the single-hop question  $Q_{1} / Q_{2}$ .
B.2 Statistics of generated datasets
For baselines and ablation study, we generate different synthetic training sets by executing different reasoning graphs. For example, we generate two datasets with single-hop questions  $\mathcal{Q}_{tbl}$  and  $\mathcal{Q}_{txt}$  for HybridQA by executing the "Table-Only" and "Text-Only" reasoning graphs, respectively. They are applied to train the ablation model A1 and A2. Table 10 summarizes all the generated datasets generated by our model MQA-QG. The column "Train Model" denotes each dataset is used to train which model in our experiments.
C Baseline: QDMR-to-Question
In this section, we introduce our proposed QDMR-to-Question, a strong unsupervised multi-hop QA baseline for HybridQA. We propose this baseline to investigate whether we can generate multi-hop questions from logical forms and compare them with our model MQA-QG.
The QDMR Representation The basic idea of QDMR-to-Question is first to generate a structured meaning representation from the source contexts and then convert it into the multi-hop question. We use the Question Decomposition Meaning Representation (QDMR) (Wolfson et al., 2020), a logical representation specially designed for multi-hop questions as the intermediate question representation. QDMR expresses complex questions via atomic operations that can be executed in sequence to answer the original question. Each atomic operation either selects a set of entities, retrieves information about their attributes, or aggregates information over entities. For example, the QDMR for the question "How many states border Colorado?" is "1) Return Colorado; 2) Return border states of #1; 3) Return the number of #2". In contrast to semantic parsing, QDMR operations are expressed through natural language.
Based on the QDMR representation, Wolfson et al. (2020) crowdsourced BREAK, a large-
Paragraph A: All Join Hands "All Join Hands" is a song by the British rock band Slade, released in 1984 as the lead single from the band's twelfth studio album "Rogues Gallery".
Paragraph B: Slade Slade are an English glam rock band from Wolverhampton. They rose to prominence during the early 1970s with 17 consecutive top 20 hits and six number ones on the UK Singles Chart.
Answer: The early 1970s
HybridQA
2004 United States Grand Prix
Pos	No	Driver	Constructor	Time	Gap
1	2	Rubens Barrichello	Ferrari	1:10.223	—
2	1	Michael Schumacher	Ferrari	1:10.400	+0.177
3	10	Takuma Sato	BAR - Honda	1:10.601	+0.378
4	9	Jenson Button	BAR - Honda	1:10.820	+0.597
5	3	Juan Pablo Montoya	Williams - BMW	1:11.062	+0.839
Jenson Alexander Lyons Button MBE (born 19 January 1980) is a British racing driver and former Formula One driver. He won the 2009 Formula One World Championship, driving for Brawn GP.
What nationality was the driver who finished in position 4 in the 2004 United States Grand Prix?
Answer: British
Figure 7: Data examples for the HotpotQA and the HybridQA dataset. Different colors (red and blue) highlight the evidences that are required to answer the multi-hop question from different sources.
Dataset	Size	Description	Train Model
HotpotQA	Qbge	129,508	Bridge-type Questions	U4. Bridge-Only
Qcom	115,162	Comparison-type Questions	U5. Comparison-Only
Qbge+com	244,220	Qbge ∪ Qcomp	U7. MQA-QG -w/o Filtration
Qhotpot	100,000	filtration(Qbge+com)	U8. MQA-QG
HybridQA	Qtbl	56,448	Table-Only Questions	A2
Qtxt	47,332	Text-Only Questions	A1
Qtxt+tbl	103,780	Qtxt ∪ Qtbl	A3
Qtxt→tbl	56,448	Text-to-Table Questions	A5
Qtbl→txt	70,661	Table-to-Text Questions	A4
Qtxt←tbl	127,109	Qtxt→tbl ∪ Qtbl→txt	U2. MQA-QG -w/o Filtration
Qhybrid	100,000	filtration(Qtxt←tbl)	U3. MQA-QG
Table 10: Basic statistics of all the generated datasets by our model MQA-QG.
scale question decomposition dataset consisting of 83,978 (QDMR, question) pairs over ten datasets.
Multi-hop Question Generation Given the table-text  $(T, D)$  as inputs, we first generate QDMR representations using two pre-defined templates that represent the Table-to-Text question and the Text-to-Table question, respectively. The templates with examples are given in Table 11. We generate QDMRs by randomly filling in the templates. Afterward, we translate the QDMR representation into a natural language question. To this end, we train a Seq2Seq model with attention (Bahdanau et al., 2014) on the BREAK dataset, where the input is a QDMR expression, and the target is the corresponding natural language form labeled by humans. We directly apply this Seq2Seq model trained on BREAK as the translator to transform our QDMR representations into multi-hop questions.
Netherlands at the European Track Championships
Medal	Championship	Name	Event
Silver	2010 Pruszkow	Tim Veldt	Men’s omnium
Bronze	2011 Apeldoorn	Kristen Wild	Women’s omnium
Gold	2013 Apeldoorn	Elis Lightlee	Women’s keirin
Gold	2013 Apeldoorn	Elis Lightlee	Women’s sprint
Kirsten Carlijn Wild (born 15 October 1982) is a Dutch professional racing cyclist, who currently rides for UCI Women's Continental Team Ceratizit-WNT Pro Cycling.
QDMR-to-Question:
What is the birthdate of the name that medal is bronze in the Netherlands at the European Track Championships?
MQA-QG:
What is the birthdate of the athlete that of Netherlands won the bronze medal in the 2011 Apelloorn?
Figure 8: Examples of generated questions for the QDMR-to-Question model and the MQA-QG.
QDMR Template	Example	Question
Table-to-Text
1) Return <column A>	1) Return Driver	What is the birthdate of the driver that pos is 4 in the 2004 United States Grand Prix?
2) Return #1 that <column B> is <row A>	2) Return #1 in Pos 4
3) Return #2 in <table title>	3) Return #2 in 2004 United States Grand Prix
4) Return what is the <text attribute> of #3	4) Return what is the birthdate of #3
Text-to-Table
1) Return <column A>	1) Return Driver	What is the pos of the driver in the 2004 United States that was born in 19 January, 1980?
2) Return #1 in <table title>	2) Return #1 in 2004 United States Grand Prix
3) Return #2 that <predicate> <object>	3) Return #2 that born 19 January 1980
4) Return what is the <column B> of #3	4) Return what is the Pos of #3
Table 11: The QDMR templates used in the QDMR-to-Question model for HybridQA.
Evaluation and Discussions As shown in Section 4.1, QDMR-to-Question achieves 21.4 F1 on the HybridQA dataset, lower than our model MQA-QG by 9.1 F1. A typical example of generated question is shown in Figure 8. We believe that the main reason for the low performance of QDMR-to-Question is that it lacks a global understanding of the table semantics. Specifically, the model lacks an understanding of the table headers' semantic meaning and the semantic relationship between different headers because table columns and table rows are randomly selected to fill in the QDMR template. For example, in Figure 8, the model generates an unnatural expression "the name that medal is bronze" because it directly copies the table header "name" and "medal" without understanding them. Instead, as our MQA-QG applies the GPT2-based table-to-text model, which encodes the entire table as an embedding, it tends to produce more natural expressions that consider the general table semantics. For the same example, MQA-QG generates a better expression "the athlete that won the bronze medal".