CNN/DM : data preprocessing

[astariul]

The link to the data of CNN/DM dataset is an already preprocessed dataset.

How can we reproduce similar dataset from the official .story files ?

[donglixp]

step 1: use corenlp to split the sentences
step 2: run BertTokenizer to obtain subword tokens
step 3: save the source text to *.src, and the target text to *.tgt

[tahmedge]

@donglixp , can you please provide details regarding how to run these 3 steps?

[donglixp]

@donglixp , can you please provide details regarding how to run these 3 steps?

def process_detokenize(chunk):
    twd = TreebankWordDetokenizer()
    tokenizer = BertTokenizer.from_pretrained(
        args.bert_model, do_lower_case=args.do_lower_case)
    r_list = []
    for idx, line in chunk:
        line = line.strip().replace('``', '"').replace('\'\'', '"').replace('`','\'')
        s_list = [twd.detokenize(x.strip().split(
            ' '), convert_parentheses=True) for x in line.split('<S_SEP>')]
        tk_list = [tokenizer.tokenize(s) for s in s_list]
        r_list.append((idx, s_list, tk_list))
    return r_list


def read_tokenized_file(fn):
    with open(fn, 'r', encoding='utf-8') as f_in:
        l_list = [l for l in f_in]
    num_pool = min(args.processes, len(l_list))
    p = Pool(num_pool)
    chunk_list = partition_all(
        int(len(l_list)/num_pool), list(enumerate(l_list)))
    r_list = []
    with tqdm(total=len(l_list)) as pbar:
        for r in p.imap_unordered(process_detokenize, chunk_list):
            r_list.extend(r)
            pbar.update(len(r))
    p.close()
    p.join()
    r_list.sort(key=lambda x: x[0])
    return [x[1] for x in r_list], [x[2] for x in r_list]


def append_sep(s_list):
    r_list = []
    for i, s in enumerate(s_list):
        r_list.append(s)
        r_list.append('[SEP_{0}]'.format(min(9, i)))
    return r_list[:-1]


## print('convert into src/tgt format')
with open(os.path.join(args.output_dir, split_out+'.src'), 'w', encoding='utf-8') as f_src, open(os.path.join(args.output_dir, split_out+'.tgt'), 'w', encoding='utf-8') as f_tgt, open(os.path.join(args.output_dir, split_out+'.slv'), 'w', encoding='utf-8') as f_slv:
    for src, tgt, lb in tqdm(zip(article_tk, summary_tk, label)):
        # source
        src_tokenized = [' '.join(s) for s in src]
        if args.src_sep_token:
            f_src.write(' '.join(append_sep(src_tokenized)))
        else:
            f_src.write(' '.join(src_tokenized))
        f_src.write('\n')
        # target (silver)
        slv_tokenized = [s for s, extract_flag in zip(
            src_tokenized, lb) if extract_flag]
        f_slv.write(' [X_SEP] '.join(slv_tokenized))
        f_slv.write('\n')
        # target (gold)
        f_tgt.write(' [X_SEP] '.join(
            [' '.join(s) for s in tgt]))
        f_tgt.write('\n')

The input should have been split by "<S_SEP>".

[tahmedge]

Thank you very much @donglixp .

[ranjeetds]

@tahmedge Did you use above script? If yes, could you please share implementation of the same?