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| import streamlit as st | |
| import pandas as pd | |
| st.set_page_config(layout="wide") | |
| with st.sidebar.expander("📍 Explanation", expanded=False): | |
| st.markdown(""" | |
| **About** | |
| This demo allows you to explore the data inside the [MRPC](https://www.microsoft.com/en-us/download/details.aspx?id=52398) dataset. | |
| It illustrates how **Word Position Deviation (WPD)** and **Lexical Deviation (LD)** can be used to find different types of [paraphrase pairs](https://direct.mit.edu/coli/article/39/3/463/1434/What-Is-a-Paraphrase) inside MRPC. | |
| By using what we observe from the data, we can find and correct numerous labelling errors inside MRPC, thus we present a revision of MRPC termed as **MRPC-R1**. | |
| **Data Display** | |
| The paraphrase pairs are displayed as **S1** and **S2** from the original MRPC (columns 1,2) and MRPC-R1 (columns 3,4), along with their labels (columns 5), showing if the label was changed or kept. **1->0** means that the pair was labelled as a paraphrase in MRPC, but corrected to non-paraphrase in MRPC-R1, meaning we rejected the paraphrase. | |
| By changing the **Display Types** option below, you can filter the displayed pairs to show pairs that were rejected (label changed from paraphrase to non-paraphrase) or corrected (inconsistencies corrected). | |
| This demo accompanies the paper ["Towards Better Characterization of Paraphrases" (ACL 2022)](https://openreview.net/forum?id=t2UJIFZVyz4), which describes in detail the methodologies used.""") | |
| with st.sidebar.expander("⚙️ Dataset Options", expanded=False): | |
| st.markdown("This allows you to switch between the MRPC train and test sets, as well as choose to display only the original paraphrase pairs (MRPC) and/or the corrected pairs (MRPC-R1).") | |
| split = st.selectbox("Dataset Split", ["train", "test"]) | |
| display = st.selectbox("Display only pairs from", ["All", "Only MRPC", "Only MRPC-R1"]) | |
| ptype = st.sidebar.radio("Display Types", ["All", | |
| "Only Paraphrases (MRPC-R1)", | |
| "Only Paraphrases (MRPC)", | |
| "Rejected Paraphrases from MRPC", | |
| "Corrected Paraphrases from MRPC"]) | |
| st.sidebar.markdown("**WPD/LD Score Filter Options**") | |
| filter_by = st.sidebar.selectbox("Filter By Scores From", ["MRPC", "MRPC-R1"]) | |
| display_range_wpd = st.sidebar.slider( | |
| "Filter by WPD Scores", min_value=0.0, max_value=1.0, value=(0.1, 0.7)) | |
| display_range_ld = st.sidebar.slider( | |
| "Filter by LD Scores", min_value=0.0, max_value=1.0, value=(0.1, 0.4)) | |
| display_scores = st.sidebar.checkbox("Display scores", value=False) | |
| with st.sidebar.expander("📍 WPD/LD Score Explanation", expanded=False): | |
| st.markdown(""" | |
| WPD and LD measure differences in the two sentences of a paraphrase pair: | |
| * WPD measures difference in the sentence structure | |
| * LD measures differences in the words used | |
| By setting WPD to a high range (>0.4) and LD to a low range (e.g. <0.1), we can find paraphrases that do not change much in words used but have very different structures. | |
| """) | |
| def load_df(split): | |
| if split == "train": | |
| df = pd.read_csv("./mrpc_train_scores.csv") | |
| else: | |
| df = pd.read_csv("./mrpc_test_scores.csv") | |
| df.reset_index(drop=True, inplace=True) | |
| return df | |
| def filter_df(df, display, ptype, filter_by, display_scores): | |
| # filter data | |
| if display == "Only MRPC": | |
| df = df.drop(["new_s1", "new_s2"], axis=1) | |
| elif display == "Only MRPC-R1": | |
| df = df.drop(["og_s1", "og_s2"], axis=1) | |
| # filter paraphrase type | |
| if ptype == "Only Paraphrases (MRPC)": | |
| condition = df.og_label == 1 | |
| df_sel = df[condition] | |
| elif ptype == "Only Paraphrases (MRPC-R1)": | |
| condition = df.new_label == 1 | |
| df_sel = df[condition] | |
| elif ptype == "Rejected Paraphrases from MRPC": | |
| condition = (df.new_label == 0) & (df.og_label == 1) | |
| df_sel = df[condition] | |
| elif ptype == "Corrected Paraphrases from MRPC": | |
| condition = df.remarks == "corrected" | |
| df_sel = df[condition] | |
| else: | |
| # all | |
| df_sel = df | |
| # sort by scores | |
| if filter_by == "MRPC": | |
| # wpd | |
| condition = (df_sel.og_wpd >= display_range_wpd[0]) & ( | |
| df_sel.og_wpd < display_range_wpd[1]) | |
| df_sel = df_sel[condition] | |
| # ld | |
| condition = (df_sel.og_ld >= display_range_ld[0]) & ( | |
| df_sel.og_ld < display_range_ld[1]) | |
| df_sel = df_sel[condition] | |
| else: | |
| # wpd | |
| condition = (df_sel.new_wpd >= display_range_wpd[0]) & ( | |
| df_sel.new_wpd < display_range_wpd[1]) | |
| df_sel = df_sel[condition] | |
| # ld | |
| condition = (df_sel.new_ld >= display_range_ld[0]) & ( | |
| df_sel.new_ld < display_range_ld[1]) | |
| df_sel = df_sel[condition] | |
| # filter scores | |
| if filter_by == "MRPC": | |
| df_sel.sort_values("og_ld", inplace=True) | |
| df_sel.sort_values("og_wpd", inplace=True) | |
| else: | |
| df_sel.sort_values("new_ld", inplace=True) | |
| df_sel.sort_values("new_wpd", inplace=True) | |
| if not display_scores: | |
| df_sel.drop(["og_ld", "og_wpd", "new_ld", "new_wpd"], | |
| axis=1, inplace=True) | |
| label_col = df_sel["og_label"].astype( | |
| str)+"->"+df_sel["new_label"].astype(str) | |
| df_sel["og/new label"] = label_col | |
| df_sel.drop(["remarks", "og_label", "new_label"], axis=1, inplace=True) | |
| return df_sel | |
| df = load_df(split) | |
| df_sel = filter_df(df, display, ptype, filter_by, display_scores) | |
| df_sel.rename(columns={"og_s1": "Original S1 (MRPC)", "og_s2": "Original S2 (MRPC)", "new_s1": "New S1 (MRPC-R1)", "new_s2": "New S2 (MRPC-R1)"}, inplace=True) | |
| st.markdown("**MRPC Paraphrase Data Explorer** (Displaying "+str(len(df_sel))+" items)") | |
| st.table(data=df_sel) | |