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ReCAM

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Commits

List of commits on branch main.
Unverified
53c44f92e2683052741d3a6c66c8ced15f1464ed

Fix configuration class

ggchhablani committed 4 years ago
Unverified
1f8272f998384a5f7a9b7f0169e388994539d0e0

Merge branch 'main' of https://github.com/gchhablani/ReCAM into main

ggchhablani committed 4 years ago
Unverified
27a3166722ec458ce589ffc97f3b5ddf068a9687

Minor Fix

ggchhablani committed 4 years ago
Unverified
43f7e81c0e77c78e09664b0219af2c76ba72d27b

added test data with labels

hharsh4799 committed 4 years ago
Unverified
52deb44c682b244b275db3689c575d6716374a5f

Fix Max Cloze Dataset

ggchhablani committed 4 years ago
Unverified
9e78b8643c9be4b975cd53c23be9b82cb362db52

Minor Fix

ggchhablani committed 4 years ago

README

The README file for this repository.

ReCAM

Our repository for the code, literature review, and results on SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning

Usage

  1. Run the setup.py file for imports to work : python setup.py install

Tasks

Imperceptibility

  • Approaches:
    • Plain GAReader (Baseline)
    • Common-sense Reasoning
    • Transformers-Based Model for Cloze-Style Question Answering
    • Incorporating The Imperceptibility Rating from the Concreteness Rating Dataset
      • The dataset is present in data/Imperceptibility/Concreteness Ratings.

Non-Specificity

  • Approaches:
    • Use WordNet's hypernyms, augment the dataset (replace nouns with their hypernyms), pretrain BERT on this augmented dataset.

To-Do

  • [x] Exploratory Data Analysis

    • [x] Find Most Common Words
    • [ ] The GloVe/CoVe representation of words in passage, words in question, and words in answer (Couple of examples).
    • [x] Word Split Based EDA (Stats)
    • [x] Bert Tokenizer Based EDA (Stats)
    • [ ] WordClouds

  • [ ] Build Basic Q/A system

    • [ ] GA Reader

  • [ ] Literature Reviews

Data

Statistics

Word/Char Python Split-Based

Task_1_train Task_1_dev Task_2_train Task_2_dev
Samples 3227 837 3318 851
Duplicated Examples 0 0 0 0
Duplicated Articles 57 4 55 2
Mean Char Count (Articles) 1548.47 1582.79 2448.93 2501.41
Std Char Count (Articles) 905.301 992.333 1788.42 1809.55
Max Char Count (Articles) 10177 9563 10370 9628
Min Char Count (Articles) 162 255 169 247
Mean Word Count (Articles) 262.44 268.608 417.727 427.048
Std Word Count (Articles) 154.534 171.253 307.248 310.994
Max Word Count (Articles) 1754 1641 1700 1651
Min Word Count (Articles) 31 41 31 45
Duplicated Questions 0 0 1 0
Mean Char Count (Questions) 137.826 137.455 148.234 149.385
Std Char Count (Questions) 31.1171 30.6809 47.7494 46.9035
Max Char Count (Questions) 389 387 443 413
Min Char Count (Questions) 32 48 31 37
Mean Word Count (Questions) 24.6771 24.4695 26.8852 27.2127
Std Word Count (Questions) 6.18453 6.04612 9.1822 9.16159
Max Word Count (Questions) 73 73 83 83
Min Word Count (Questions) 6 9 7 6

BertTokenizer Based

Task_1_train Task_1_dev Task_2_train Task_2_dev
Mean Token Count (Articles) 332.603 341.452 528.58 539.303
Std Token Count (Articles) 193.729 217.673 387.756 396.286
Max Token Count (Articles) 2206 2043 2188 2272
Min Token Count (Articles) 38 52 46 52
Mean Token Count (Questions) 28.6319 28.3441 30.9629 31.1657
Std Token Count (Questions) 6.83765 6.75112 10.0225 9.98314
Max Token Count (Questions) 84 84 91 89
Min Token Count (Questions) 9 11 9 8

Note :

  • Word is based on .split() on Python strings, including all the words (stopwords). We can also use torch/nltk tokenizers to get tokens and check their lengths.
  • The spaces are also included in character counting.
  • No preprocessing was done for either of the statistics.
  • For questions, we do not remove @placeholder.

Most Frequent Words

Note :

  • Tokens were made using NLTK Tokenizer, and only alphanumeric characters, after removing stop words were used.

Articles

Task_1_train Task_1_dev Task_2_train Task_2_dev
Unique Token Count 41087 20447 53285 25779
Total Count 482713 128258 775342 203877
Task_1_train Task_1_dev Task_2_train Task_2_dev
Word Count Frequency Word Count Frequency Word Count Frequency Word Count Frequency
0 said 7978 0.0165274 said 1940 0.0151258 said 9594 0.0123739 said 2524 0.01238
1 mr 2465 0.00510655 mr 701 0.00546555 mr 3549 0.00457733 mr 993 0.00487058
2 would 2194 0.00454514 would 593 0.00462349 would 3539 0.00456444 would 954 0.00467929
3 also 2019 0.00418261 also 506 0.00394517 people 3335 0.00430133 people 872 0.00427709
4 people 1897 0.00392987 one 486 0.00378924 one 3182 0.004104 one 814 0.0039926
5 one 1705 0.00353212 people 481 0.00375025 also 2784 0.00359067 also 675 0.00331082
6 last 1461 0.00302664 first 395 0.00307973 says 2201 0.00283875 two 562 0.00275656
7 two 1349 0.00279462 last 391 0.00304854 two 2112 0.00272396 says 547 0.00268299
8 first 1344 0.00278426 new 372 0.0029004 years 2084 0.00268785 new 545 0.00267318
9 new 1341 0.00277805 told 349 0.00272108 new 2081 0.00268398 time 532 0.00260942
10 year 1262 0.00261439 two 346 0.00269769 time 2064 0.00266205 us 531 0.00260451
11 years 1248 0.00258539 us 342 0.0026665 could 2002 0.00258209 could 517 0.00253584
12 could 1222 0.00253152 years 340 0.00265091 last 1957 0.00252405 years 516 0.00253094
13 told 1203 0.00249216 year 329 0.00256514 first 1944 0.00250728 first 509 0.0024966
14 us 1187 0.00245902 time 307 0.00239361 us 1820 0.00234735 told 493 0.00241812
15 time 1159 0.00240101 could 291 0.00226886 year 1768 0.00228028 year 462 0.00226607
16 government 1036 0.0021462 bbc 256 0.00199598 like 1727 0.0022274 last 455 0.00223174
17 made 940 0.00194733 added 246 0.00191801 told 1659 0.0021397 bbc 418 0.00205026
18 bbc 894 0.00185203 made 236 0.00184004 police 1504 0.00193979 like 408 0.00200121
19 police 892 0.00184789 get 227 0.00176987 get 1476 0.00190368 government 404 0.00198159

Questions

Task_1_train Task_1_dev Task_2_train Task_2_dev
Unique Token Count 10032 4602 11098 4966
Total Count 43683 11289 47297 12255
Task_1_train Task_1_dev Task_2_train Task_2_dev
Word Count Frequency Word Count Frequency Word Count Frequency Word Count Frequency
0 placeholder 3227 0.0738731 placeholder 837 0.074143 placeholder 3318 0.0701524 placeholder 851 0.069441
1 said 268 0.00613511 new 69 0.00611214 new 229 0.00484174 new 71 0.00579355
2 year 230 0.00526521 says 62 0.00549207 year 218 0.00460917 two 60 0.00489596
3 new 222 0.00508207 year 56 0.00496058 two 209 0.00441888 says 57 0.00465116
4 says 197 0.00450976 us 48 0.00425193 one 201 0.00424974 year 54 0.00440636
5 two 172 0.00393746 said 46 0.00407476 said 200 0.0042286 said 54 0.00440636
6 first 159 0.00363986 first 43 0.00380902 man 191 0.00403831 us 49 0.00399837
7 world 158 0.00361697 two 43 0.00380902 police 185 0.00391145 people 48 0.00391677
8 one 148 0.00338805 world 38 0.00336611 people 177 0.00374231 one 47 0.00383517
9 man 143 0.00327358 uk 35 0.00310036 first 174 0.00367888 man 46 0.00375357
10 people 141 0.0032278 one 34 0.00301178 world 168 0.00355202 first 44 0.00359037
11 us 129 0.00295309 man 32 0.00283462 years 154 0.00325602 years 37 0.00301918
12 police 127 0.00290731 people 32 0.00283462 us 147 0.00310802 police 36 0.00293758
13 uk 127 0.00290731 police 31 0.00274604 uk 133 0.00281202 uk 32 0.00261118
14 wales 118 0.00270128 former 31 0.00274604 says 130 0.00274859 world 31 0.00252958
15 years 115 0.0026326 england 31 0.00274604 three 113 0.00238916 could 31 0.00252958
16 former 112 0.00256393 wales 29 0.00256887 england 111 0.00234687 city 28 0.00228478
17 government 110 0.00251814 league 29 0.00256887 old 105 0.00222001 found 27 0.00220318
18 could 109 0.00249525 years 28 0.00248029 could 102 0.00215658 may 27 0.00220318
19 city 106 0.00242657 three 25 0.00221455 last 100 0.0021143 bbc 27 0.00220318

Note:

  • BERT Tokenizer based splits
    • Process - Take passages, convert to lowercase, remove all non-alphanumeric characters, tokenize, then remove stop words.

Articles

Task_1_train Task_1_dev Task_2_train Task_2_dev
Unique Token Count 21952 15858 23842 18157
Total Count 559481 148611 898160 235177
Task_1_train Task_1_dev Task_2_train Task_2_dev
Word Count Frequency Word Count Frequency Word Count Frequency Word Count Frequency
0 ##s 8094 0.014467 ##s 2393 0.0161024 ##s 13511 0.015043 ##s 3477 0.0147846
1 said 7994 0.0142882 said 1941 0.0130609 said 9597 0.0106852 said 2525 0.0107366
2 mr 2466 0.00440766 mr 702 0.00472374 ##t 4282 0.00476752 ##t 1068 0.00454126
3 ##t 2266 0.00405018 ##t 674 0.00453533 mr 3550 0.00395253 mr 993 0.00422235
4 would 2204 0.00393937 would 595 0.00400374 would 3549 0.00395141 would 964 0.00409904
5 also 2020 0.00361049 one 513 0.00345197 one 3348 0.00372762 people 873 0.0037121
6 people 1897 0.00339064 also 506 0.00340486 people 3335 0.00371315 one 855 0.00363556
7 one 1822 0.00325659 people 481 0.00323664 also 2786 0.0031019 also 675 0.00287018
8 two 1479 0.00264352 first 408 0.00274542 two 2277 0.00253518 two 611 0.00259804
9 last 1477 0.00263995 last 399 0.00268486 says 2201 0.00245057 new 557 0.00236843
10 first 1417 0.0025327 new 380 0.00255701 new 2129 0.0023704 says 547 0.00232591
11 new 1372 0.00245227 two 379 0.00255028 years 2084 0.0023203 us 546 0.00232166
12 year 1281 0.00228962 told 349 0.00234841 time 2076 0.00231139 time 540 0.00229614
13 years 1248 0.00223064 us 345 0.0023215 first 2056 0.00228912 first 525 0.00223236
14 could 1224 0.00218774 years 340 0.00228785 could 2007 0.00223457 could 518 0.0022026
15 ##m 1219 0.0021788 ##e 339 0.00228112 last 1975 0.00219894 years 516 0.00219409
16 us 1206 0.00215557 year 330 0.00222056 us 1844 0.00205309 told 493 0.00209629
17 told 1203 0.00215021 bbc 328 0.0022071 year 1793 0.0019963 bbc 475 0.00201976
18 time 1173 0.00209659 time 311 0.00209271 ##ing 1785 0.0019874 ##e 472 0.002007
19 bbc 1112 0.00198756 could 291 0.00195813 ##e 1761 0.00196068 year 468 0.00198999

Questions

Task_1_train Task_1_dev Task_2_train Task_2_dev
Unique Token Count 9771 4876 10702 5251
Total Count 51629 13284 55858 14331
Task_1_train Task_1_dev Task_2_train Task_2_dev
Word Count Frequency Word Count Frequency Word Count Frequency Word Count Frequency
0 place 3256 0.0630653 place 844 0.0635351 place 3349 0.0599556 place 859 0.05994
1 ##holder 3227 0.0625036 ##holder 837 0.0630081 ##holder 3318 0.0594006 ##holder 851 0.0593818
2 said 272 0.00526836 new 69 0.00519422 ##s 333 0.00596155 new 72 0.00502407
3 year 230 0.00445486 ##s 66 0.00496838 new 230 0.00411758 ##s 72 0.00502407
4 ##s 223 0.00431928 says 62 0.00466727 year 218 0.00390275 two 60 0.00418673
5 new 222 0.00429991 year 56 0.0042156 two 209 0.00374163 says 57 0.00397739
6 says 197 0.00381568 us 48 0.00361337 one 201 0.00359841 year 54 0.00376806
7 two 172 0.00333146 said 46 0.00346281 said 200 0.00358051 said 54 0.00376806
8 first 160 0.00309903 first 43 0.00323698 man 196 0.0035089 man 50 0.00348894
9 world 158 0.0030603 two 43 0.00323698 police 185 0.00331197 us 49 0.00341916
10 man 150 0.00290534 world 38 0.00286058 people 177 0.00316875 people 48 0.00334938
11 one 148 0.00286661 uk 36 0.00271003 first 174 0.00311504 one 47 0.0032796
12 people 141 0.00273102 man 35 0.00263475 world 168 0.00300763 first 44 0.00307027
13 uk 135 0.00261481 one 34 0.00255947 years 154 0.00275699 years 37 0.00258182
14 us 130 0.00251796 people 32 0.00240891 us 147 0.00263167 police 36 0.00251204
15 police 129 0.0024986 police 31 0.00233363 uk 142 0.00254216 uk 33 0.0023027
16 wales 118 0.00228554 former 31 0.00233363 says 130 0.00232733 world 32 0.00223292
17 years 115 0.00222743 england 31 0.00233363 three 113 0.00202299 could 31 0.00216314
18 former 112 0.00216932 wales 29 0.00218308 england 111 0.00198718 city 28 0.00195381
19 government 110 0.00213059 league 29 0.00218308 old 105 0.00187977 found 27 0.00188403

References

Imperceptibility

Non-Specificity