diff --git a/seacrowd/sea_datasets/lexitron/__init__.py b/seacrowd/sea_datasets/lexitron/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/seacrowd/sea_datasets/lexitron/lexitron.py b/seacrowd/sea_datasets/lexitron/lexitron.py
new file mode 100644
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+++ b/seacrowd/sea_datasets/lexitron/lexitron.py
@@ -0,0 +1,295 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Corpus-based dictionary of Thai and English languages. \
+    This dataset contains frequently-used words from trusted \
+    publications such as novels, academic documents and newspaper. \
+    The dataset link contains Thai-English and English-Thai lexicons. \
+    Thai-English vocabulary consists of vocabulary, type of word \
+    (part of speech), translation, synonym (synonym) and sample sentences \
+    with a list of Thai-> English words, 53,000 words and English vocabulary \
+    list -> Thai, 83,000 words.
+"""
+import os
+import re
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+# There are no citations available for this dataset.
+_CITATION = ""
+
+_DATASETNAME = "lexitron"
+
+_DESCRIPTION = """
+Corpus-based dictionary of Thai and English languages. \
+    This dataset contains frequently-used words from trusted \
+    publications such as novels, academic documents and newspaper. \
+    The dataset link contains Thai-English and English-Thai lexicons. \
+    Thai-English vocabulary consists of vocabulary, type of word \
+    (part of speech), translation, synonym (synonym) and sample sentences \
+    with a list of Thai-> English words, 53,000 words and English vocabulary \
+    list -> Thai, 83,000 words.
+"""
+
+_HOMEPAGE = "https://opend-portal.nectec.or.th/dataset/lexitron-2-0"
+
+_LANGUAGES = ["tha"]
+
+_LICENSE = Licenses.OTHERS.value
+
+_LOCAL = False
+
+_URLS = {
+    "telex": "https://opend-portal.nectec.or.th/dataset/bdd85296-9398-499f-b3a7-aab85042d3f9/resource/761924ea-937f-4be3-afe1-c031c754fa39/download/lexitron_2.0.zip",
+    "etlex": "https://opend-portal.nectec.or.th/dataset/bdd85296-9398-499f-b3a7-aab85042d3f9/resource/761924ea-937f-4be3-afe1-c031c754fa39/download/lexitron_2.0.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "1.0.0"
+
+
+class LEXiTRONDataset(datasets.GeneratorBasedBuilder):
+    """
+    Corpus-based dictionary of Thai and English languages. \
+    This dataset contains frequently-used words from trusted \
+    publications such as novels, academic documents and newspaper. \
+    The dataset link contains Thai-English and English-Thai lexicons. \
+    Thai-English vocabulary consists of vocabulary, type of word \
+    (part of speech), translation, synonym (synonym) and sample sentences \
+    with a list of Thai-> English words, 53,000 words and English vocabulary \
+    list -> Thai, 83,000 words.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+    SEACROWD_SCHEMA_NAME = "t2t"
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_telex_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}_telex",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_telex_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=f"{_DATASETNAME}_telex",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_etlex_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}_etlex",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_etlex_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=f"{_DATASETNAME}_etlex",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "[dataset_name]_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+
+            translation_type = self.config.name.split("_")[1]
+
+            if translation_type == "telex":
+                features = datasets.Features(
+                    {
+                        "id": datasets.Value("int64"),
+                        "tsearch": datasets.Value("string"),
+                        "tentry": datasets.Value("string"),
+                        "eentry": datasets.Value("string"),
+                        "tcat": datasets.Value("string"),
+                        "tsyn": datasets.Value("string"),
+                        "tsample": datasets.Value("string"),
+                        "tdef": datasets.Value("string"),
+                    }
+                )
+
+            elif translation_type == "etlex":
+                features = datasets.Features(
+                    {"id": datasets.Value("int64"), "esearch": datasets.Value("string"), "eentry": datasets.Value("string"), "tentry": datasets.Value("string"), "ecat": datasets.Value("string"), "esyn": datasets.Value("string")}
+                )
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.text2text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        translation_type = self.config.name.split("_")[1]
+        data_dir = dl_manager.download_and_extract(_URLS[translation_type])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, f"LEXiTRON_2.0/{translation_type}"),
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        translation_type = self.config.name.split("_")[1]
+
+        if translation_type == "telex":
+
+            with open(filepath, "r", encoding="latin-1") as file:
+                data = file.read()
+
+            pattern = r"<Doc>(.*?)</Doc>"
+            docs = re.findall(pattern, data, re.DOTALL)
+
+            doc_data = []
+
+            for doc in docs:
+                tsearch = tentry = eentry = tcat = tsyn = tsample = tdef = id = None
+
+                tsearch_match = re.search(r"<tsearch>(.*?)</tsearch>", doc)
+                if tsearch_match:
+                    tsearch = tsearch_match.group(1)
+
+                tentry_match = re.search(r"<tentry>(.*?)</tentry>", doc)
+                if tentry_match:
+                    tentry = tentry_match.group(1)
+
+                eentry_match = re.search(r"<eentry>(.*?)</eentry>", doc)
+                if eentry_match:
+                    eentry = eentry_match.group(1)
+
+                tcat_match = re.search(r"<tcat>(.*?)</tcat>", doc)
+                if tcat_match:
+                    tcat = tcat_match.group(1)
+
+                tsyn_match = re.search(r"<tsyn>(.*?)</tsyn>", doc)
+                if tsyn_match:
+                    tsyn = tsyn_match.group(1)
+
+                tsample_match = re.search(r"<tsample>(.*?)</tsample>", doc)
+                if tsample_match:
+                    tsample = tsample_match.group(1)
+
+                tdef_match = re.search(r"<tdef>(.*?)</tdef>", doc)
+                if tdef_match:
+                    tdef = tdef_match.group(1)
+
+                id_match = re.search(r"<id>(.*?)</id>", doc)
+                if id_match:
+                    id = id_match.group(1)
+
+                doc_data.append({"id": id, "tsearch": tsearch, "tentry": tentry, "eentry": eentry, "tcat": tcat, "tsyn": tsyn, "tsample": tsample, "tdef": tdef})
+
+            df = pd.DataFrame(doc_data)
+
+        if translation_type == "etlex":
+
+            with open(filepath, "r", encoding="latin-1") as file:
+                data = file.read()
+
+            pattern = r"<Doc>(.*?)</Doc>"
+            docs = re.findall(pattern, data, re.DOTALL)
+
+            doc_data = []
+
+            for doc in docs:
+                esearch = eentry = tentry = ecat = esyn = id = None
+
+                esearch_match = re.search(r"<esearch>(.*?)</esearch>", doc)
+                if esearch_match:
+                    esearch = esearch_match.group(1)
+
+                eentry_match = re.search(r"<eentry>(.*?)</eentry>", doc)
+                if eentry_match:
+                    eentry = eentry_match.group(1)
+
+                tentry_match = re.search(r"<tentry>(.*?)</tentry>", doc)
+                if tentry_match:
+                    tentry = tentry_match.group(1)
+
+                ecat_match = re.search(r"<ecat>(.*?)</ecat>", doc)
+                if ecat_match:
+                    ecat = ecat_match.group(1)
+
+                esyn_match = re.search(r"<esyn>(.*?)</esyn>", doc)
+                if esyn_match:
+                    esyn = esyn_match.group(1)
+
+                id_match = re.search(r"<id>(.*?)</id>", doc)
+                if id_match:
+                    id = id_match.group(1)
+
+                doc_data.append({"id": id, "esearch": esearch, "eentry": eentry, "tentry": tentry, "ecat": ecat, "esyn": esyn})
+
+            df = pd.DataFrame(doc_data)
+
+        for index, row in df.iterrows():
+
+            if self.config.schema == "source":
+                example = row.to_dict()
+
+            elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+
+                if translation_type == "telex":
+                    example = {
+                        "id": str(index),
+                        "text_1": str(row["tentry"]),
+                        "text_2": str(row["eentry"]),
+                        "text_1_name": "tentry",
+                        "text_2_name": "eentry",
+                    }
+
+                if translation_type == "etlex":
+                    example = {
+                        "id": str(index),
+                        "text_1": str(row["eentry"]),
+                        "text_2": str(row["tentry"]),
+                        "text_1_name": "eentry",
+                        "text_2_name": "tentry",
+                    }
+
+            yield index, example